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# AI/ML Engineer III

- **Company:** Technergetics, LLC
- **Location:** Utica, NY, United States (Remote available)
- **Salary:** $125,000.0 - $175,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Computer-Aided Design, Geographic Information Systems, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Software Applications, Automation of Tests, Unit Testing, Microsoft Azure, C++ (Programming Language), Command-Line Interface, Cloud Computing, Code Review, Encodings, Computer Engineering, Continuous Delivery, Continuous Integration, Data Transformation, Data Migration, Data Security, Software Debugging, Linux, Distributed Computing Environment, Django Web Framework, Graph Database, Python (Programming Language), Machine Learning, Node.Js, Scrum Methodology, Quantum Computing, Resource Description Framework (RDF), Software Safety, Server Administration, Software Engineering, Software Factory, Software Systems, Data Streaming, Systems Integration, Web Applications, Datadog, Qdrant, Google Cloud, Cloud Platform System, Pytorch, LangChain, DeepSpeed, ReactJS, Retrieval-Augmented Generation, Transfer Learning, On-device Inference, Large Language Models, Prompt Engineering, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Gitlab, Discretization, Pytest, Pgvector, Containerization, LangSmith, Kubernetes, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, HuggingFace, CrewAI, Weaviate, AutoGen, Milvus, Slurm, Ollama, Triton Inference Server, Machine Learning Operations, React Native, FAISS, TensorRT, Asynchronous Programming, Evaluation of Large Language Models, Graph RAG, Restful APIs, Model Context Protocol, VLLM, Data Pipelines, Devsecops, Software Library, Docker, Golang
- **Published:** October 18, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-ml-engineer-iii-utica-ny--8a3a476f-48f5-4957-8c8b-adeeaf0268e2

## About the Role

This position generally requires a Master's degree from an accredited college or university in computer science, computer engineering, artificial intelligence, machine learning, or a closely related discipline. A Ph.D. in one of these fields is strongly preferred. A Bachelor's degree in one of these fields, combined with seven or more years of directly relevant professional experience, will be considered in lieu of a Master's degree., * At minimum, three years of professional experience in machine learning or AI systems engineering, including at least one year building with large language models or other foundation models in a production setting

 * Strong proficiency in Python, including asynchronous programming and modern packaging and dependency management
 * Working knowledge of server-side development (API definitions, REST services, streaming and asynchronous services, etc.)
 * Fluency with containerization and deployment frameworks such as Kubernetes or Docker, including GPU scheduling and resource management for training and inference workloads
 * Hands-on work with at least one major cloud platform (AWS, Azure, or Google Cloud)
 * Comfort with Linux platforms and command-line environments
 * Familiarity with Continuous Delivery/Continuous Integration (DevSecOps, GitLab Pipelines, etc.)
 * Proficiency with automated testing in Python (pytest), including regression suites for non-deterministic model and agent behavior

Artificial Intelligence and Machine Learning

 * Demonstrated ability to train and deploy machine learning models with PyTorch and the Hugging Face ecosystem (transformers, datasets, accelerate)
 * A track record of building applications on top of large language models, including prompt engineering, structured output, and context management
 * Fluency with agentic frameworks and patterns such as LangGraph, LangChain, CrewAI, AutoGen, Pydantic AI, or vendor agent SDKs, including multi-step tool use, planning, memory, and state management, and with integrating models, tools, and data sources through open standards such as Model Context Protocol (MCP)
 * Practical command of retrieval-augmented generation, including chunking and embedding strategy, vector databases (pgvector, Milvus, Qdrant, Weaviate, FAISS), and hybrid or re-ranked retrieval
 * Ability to design and run LLM evaluation, including task-specific benchmarks, golden datasets, LLM-as-judge methods, and tracing and observability tooling (LangSmith, Langfuse, Arize, Weights & Biases)
 * Command of model adaptation techniques including fine-tuning, LoRA/PEFT, quantization, and distillation, and the judgment to know when adaptation is preferable to prompting or retrieval
 * Proven ability to serve models in production with inference frameworks such as vLLM, TensorRT-LLM, Triton Inference Server, Ollama, or ONNX Runtime, including latency, throughput, and cost tradeoffs, as well as deployment to edge or resource-constrained environments, including on-device inference
 * Grounding in AI safety and assurance practices, including guardrails, input and output filtering, prompt injection mitigation, and human-in-the-loop design
 * Direct work with multimodal models and cross-modal embedding across text, imagery, video, audio, or geospatial data

Leadership and Collaboration

 * Demonstrated leadership on technical tasks and/or technical teams, including Agile software development and leading one or more tasks to completion
 * Excellent communication and teamwork skills, including the ability to explain technical tradeoffs to non-technical stakeholders and customers, * Exposure to DoD cloud and software factory environments such as Platform One, BESPIN, AF Cloud One, DAF CLOUDworks, or AWS GovCloud, and with ATO and RMF processes at IL4/IL5
 * Familiarity with DoD and federal AI policy and governance, including CDAO Responsible AI guidance
 * Work with distributed training frameworks and schedulers (DeepSpeed, FSDP, Ray, Slurm)
 * Knowledge of knowledge graphs, ontologies, or Resource Description Framework (RDF), particularly as applied to graph-based retrieval (GraphRAG) and grounding
 * Background in developing modern full-stack web applications with frameworks such as Node, React, React Native, or Django
 * Basic working knowledge of Go, Java, C++, or other compiled languages
 * Contributions to open source AI/ML projects, or published applied AI research

Clearance

Selected applicants will undergo a security investigation and must meet and maintain eligibility for, at minimum, Top Secret access to classified information.

## Description

AI/ML Engineer IIITechnergetics - Utica/Rome, NY areaA Note About Our AI-Assisted Interview ProcessWe use an AI application, "Alex Taylor," to conduct first-round interviews for this position. The interview takes approximately 35 minutes. If we would like to interview you, you will receive an email invitation from "Alex" within ten business days of your application.Alex is available 24/7, which lets us conduct far more first-round interviews than our human staff's schedule alone would allow. Technergetics HR (and additional staff, as applicable) reviews every first-round interview. AI supports our decision-making, but all decisions about who advances to a first or second interview are made by our human staff. Candidates selected for a second-round interview will meet with the HR director and hiring manager.Any data collected during the interview process, including AI-generated insights, is handled with care and confidentiality, in compliance with applicable data protection laws. Your responses are processed only to provide feedback on your skills and knowledge. Data is stored securely and will not be shared with third parties without your consent.We understand some candidates may be hesitant to interview with an AI application - it is by no means perfect at this time. But as a company dedicated to research and development in AI/ML and other technologies, we see this as a chance to practice what we preach.Opportunity OverviewTechnergetics is looking for an AI/ML Engineer III to design, develop, and deploy advanced AI capabilities alongside a high-performing team of full-stack developers. This role centers on building production systems around foundation models, including agentic workflows, retrieval-augmented generation, and multimodal machine learning, for demanding government and commercial customers.Contingent Position: This position is contingent upon contract award and funding.Position DetailsSalary Range: $125,000-$175,000 annually. The final offer depends on how many position qualifications the candidate meets, as well as education and experience. This is a full-time, exempt position.Location, Travel, and Remote WorkCandidates who are located within, or relocate to, a commutable distance of the Utica/Rome area can expect to be onsite 20% of their workweek, for access to company and AFRL (Air Force Research Lab) facilities, secure data, and customers. A relocation signing bonus may be available.Remote candidates outside a commutable distance to Utica/Rome will still be considered but may need to travel to the Utica/Rome area quarterly or more often, depending on company and client needs.This position also involves approximately 5%-10% travel to customer and client sites outside the Utica-Rome, NY area.Due to the security clearance required for this position, only U.S. citizens are eligible to apply, per Executive Order 12968 (Access to Classified Information).Responsibilities and DutiesThe successful candidate will work on one or more of our Machine Learning (ML) software products, with day-to-day activities that include:Leading the design, development, and deployment of multi-modal machine learning architectures, including models and algorithms, to solve complex mission and business problemsDesigning and building agentic systems on top of large language models for operational deploymentImplementing retrieval-augmented generation pipelines that ground model outputs against authoritative data sourcesDefining and running evaluation for model and agent behavior in productionOptimizing model inference for production and edge/DDIL (denied, degraded, intermittent, and limited bandwidth) deployment scenariosApplying AI assurance practices and documenting model limitations to support accreditation and customer reviewIntegrating state-of-the-art machine learning libraries, foundation models, and agent frameworks into existing software applicationsDeveloping and maintaining data pipelines and supporting software for collecting, preprocessing, and transforming data for machine learning tasksDesigning software solutions, algorithms, and cloud architectures needed to satisfy product features and functionality defined by the product owner and other stakeholders in a production environmentLeading, coaching, and mentoring junior data scientists, engineers, and other staffContributing to phases of the software development life cycle, including functional analysis, technical requirements, technical design, prototyping, coding, testing, deployment, data migration, and supportParticipating in daily scrums and working with the scrum master and scrum team to organize and prioritize workload through story-pointing, supporting delivery timelines and prioritiesCollaborating with cross-functional teams to understand business requirements and translate them into machine learning solutionsPerforming unit testing and debugging to identify and fix software defects, and contributing to code reviews with constructive feedback to peersStaying current on new AI/ML approaches, frameworks, and industry trendsServing as an AI subject matter expert for small teams of researchers and engineers on advanced R&D projects funded by government and/or commercial customers, and contributing to or leading proposal writing for new opportunities within your area of expertiseEducation and CertificationsThis position generally requires a Master's degree from an accredited college or university in computer science, computer engineering, artificial intelligence, machine learning, or a closely related discipline. A Ph.D. in one of these fields is strongly preferred. A Bachelor's degree in one of these fields, combined with seven or more years of directly relevant professional experience, will be considered in lieu of a Master's degree.QualificationsExperience and Foundational EngineeringAt minimum, three years of professional experience in machine learning or AI systems engineering, including at least one year building with large language models or other foundation models in a production settingStrong proficiency in Python, including asynchronous programming and modern packaging and dependency managementWorking knowledge of server-side development (API definitions, REST services, streaming and asynchronous services, etc.)Fluency with containerization and deployment frameworks such as Kubernetes or Docker, including GPU scheduling and resource management for training and inference workloadsHands-on work with at least one major cloud platform (AWS, Azure, or Google Cloud)Comfort with Linux platforms and command-line environmentsFamiliarity with Continuous Delivery/Continuous Integration (DevSecOps, GitLab Pipelines, etc.)Proficiency with automated testing in Python (pytest), including regression suites for non-deterministic model and agent behaviorArtificial Intelligence and Machine LearningDemonstrated ability to train and deploy machine learning models with PyTorch and the Hugging Face ecosystem (transformers, datasets, accelerate)A track record of building applications on top of large language models, including prompt engineering, structured output, and context managementFluency with agentic frameworks and patterns such as LangGraph, LangChain, CrewAI, AutoGen, Pydantic AI, or vendor agent SDKs, including multi-step tool use, planning, memory, and state management, and with integrating models, tools, and data sources through open standards such as Model Context Protocol (MCP)Practical command of retrieval-augmented generation, including chunking and embedding strategy, vector databases (pgvector, Milvus, Qdrant, Weaviate, FAISS), and hybrid or re-ranked retrievalAbility to design and run LLM evaluation, including task-specific benchmarks, golden datasets, LLM-as-judge methods, and tracing and observability tooling (LangSmith, Langfuse, Arize, Weights & Biases)Command of model adaptation techniques including fine-tuning, LoRA/PEFT, quantization, and distillation, and the judgment to know when adaptation is preferable to prompting or retrievalProven ability to serve models in production with inference frameworks such as vLLM, TensorRT-LLM, Triton Inference Server, Ollama, or ONNX Runtime, including latency, throughput, and cost tradeoffs, as well as deployment to edge or resource-constrained environments, including on-device inferenceGrounding in AI safety and assurance practices, including guardrails, input and output filtering, prompt injection mitigation, and human-in-the-loop designDirect work with multimodal models and cross-modal embedding across text, imagery, video, audio, or geospatial dataLeadership and CollaborationDemonstrated leadership on technical tasks and/or technical teams, including Agile software development and leading one or more tasks to completionExcellent communication and teamwork skills, including the ability to explain technical tradeoffs to non-technical stakeholders and customersNice to HaveExposure to DoD cloud and software factory environments such as Platform One, BESPIN, AF Cloud One, DAF CLOUDworks, or AWS GovCloud, and with ATO and RMF processes at IL4/IL5Familiarity with DoD and federal AI policy and governance, including CDAO Responsible AI guidanceWork with distributed training frameworks and schedulers (DeepSpeed, FSDP, Ray, Slurm)Knowledge of knowledge graphs, ontologies, or Resource Description Framework (RDF), particularly as applied to graph-based retrieval (GraphRAG) and groundingBackground in developing modern full-stack web applications with frameworks such as Node, React, React Native, or DjangoBasic working knowledge of Go, Java, C++, or other compiled languagesContributions to open source AI/ML projects, or published applied AI researchClearanceSelected applicants will undergo a security investigation and must meet and maintain eligibility for, at minimum, Top Secret access to classified information.BenefitsOur benefits package includes health, life, disability, dental, and vision insurance, plus a 401(k) plan with a 3% company contribution and 3% company match.Additional perks include:Generous Paid Time Off, including a PTO "gift day" for your birthday11 federal holidays per yearThree weeks of paid maternity/paternity leaveAnnual technology allowanceReferral bonuses and professional recognition awardsHealthcare stipendsTuition/education reimbursement (once eligibility requirements are met)Flexible daily start and stop times for most projects and positionsCompany DescriptionTechnergetics is a U.S.-based company headquartered in Utica, NY, with employees and clients located throughout the country. The Utica/Rome area is a hub of cutting-edge cyber technology research, bolstered by the Griffiss Business & Technology Park's tenants and facilities, including the Air Force Research Lab (AFRL). At Technergetics, we work with a wide variety of technologies, including mobile and web apps, quantum computing, machine learning and artificial intelligence, AI-enabled edge devices, and more. Beware of Fraudulent Job Offers and PostingsTechnergetics will never extend an offer of employment without a thorough interview process that includes a face-to-face interview - either in person or via a virtual Teams meeting - from an official Technergetics email address (@techngs.com). If you receive correspondence from any other email address, it is a scam. Technergetics does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
AI/ML Engineer IIITechnergetics - Utica/Rome, NY areaA Note About Our AI-Assisted Interview ProcessWe use an AI application, "Alex Taylor," to conduct first-round interviews for this position. The interview takes approximately 35 minutes. If we would like to interview you, you will receive an email invitation from "Alex" within ten business days of your application.Alex is available 24/7, which lets us conduct far more first-round interviews than our human staff's schedule alone would allow. Technergetics HR (and additional staff, as applicable) reviews every first-round interview. AI supports our decision-making, but all decisions about who advances to a first or second interview are made by our human staff. Candidates selected for a second-round interview will meet with the HR director and hiring manager.Any data collected during the interview process, including AI-generated insights, is handled with care and confidentiality, in compliance with applicable data protection laws. Your responses are processed only to provide feedback on your skills and knowledge. Data is stored securely and will not be shared with third parties without your consent.We understand some candidates may be hesitant to interview with an AI application - it is by no means perfect at this time. But as a company dedicated to research and development in AI/ML and other technologies, we see this as a chance to practice what we preach.Opportunity OverviewTechnergetics is looking for an AI/ML Engineer III to design, develop, and deploy advanced AI capabilities alongside a high-performing team of full-stack developers. This role centers on building production systems around foundation models, including agentic workflows, retrieval-augmented generation, and multimodal machine learning, for demanding government and commercial customers.Contingent Position: This position is contingent upon contract award and funding.Position DetailsSalary Range: $125,000-$175,000 annually. The final offer depends on how many position qualifications the candidate meets, as well as education and experience. This is a full-time, exempt position.Location, Travel, and Remote WorkCandidates who are located within, or relocate to, a commutable distance of the Utica/Rome area can expect to be onsite 20% of their workweek, for access to company and AFRL (Air Force Research Lab) facilities, secure data, and customers. A relocation signing bonus may be available.Remote candidates outside a commutable distance to Utica/Rome will still be considered but may need to travel to the Utica/Rome area quarterly or more often, depending on company and client needs.This position also involves approximately 5%-10% travel to customer and client sites outside the Utica-Rome, NY area.Due to the security clearance required for this position, only U.S. citizens are eligible to apply, per Executive Order 12968 (Access to Classified Information).Responsibilities and DutiesThe successful candidate will work on one or more of our Machine Learning (ML) software products, with day-to-day activities that include:Leading the design, development, and deployment of multi-modal machine learning architectures, including models and algorithms, to solve complex mission and business problemsDesigning and building agentic systems on top of large language models for operational deploymentImplementing retrieval-augmented generation pipelines that ground model outputs against authoritative data sourcesDefining and running evaluation for model and agent behavior in productionOptimizing model inference for production and edge/DDIL (denied, degraded, intermittent, and limited bandwidth) deployment scenariosApplying AI assurance practices and documenting model limitations to support accreditation and customer reviewIntegrating state-of-the-art machine learning libraries, foundation models, and agent frameworks into existing software applicationsDeveloping and maintaining data pipelines and supporting software for collecting, preprocessing, and transforming data for machine learning tasksDesigning software solutions, algorithms, and cloud architectures needed to satisfy product features and functionality defined by the product owner and other stakeholders in a production environmentLeading, coaching, and mentoring junior data scientists, engineers, and other staffContributing to phases of the software development life cycle, including functional analysis, technical requirements, technical design, prototyping, coding, testing, deployment, data migration, and supportParticipating in daily scrums and working with the scrum master and scrum team to organize and prioritize workload through story-pointing, supporting delivery timelines and prioritiesCollaborating with cross-functional teams to understand business requirements and translate them into machine learning solutionsPerforming unit testing and debugging to identify and fix software defects, and contributing to code reviews with constructive feedback to peersStaying current on new AI/ML approaches, frameworks, and industry trendsServing as an AI subject matter expert for small teams of researchers and engineers on advanced R&D projects funded by government and/or commercial customers, and contributing to or leading proposal writing for new opportunities within your area of expertiseEducation and CertificationsThis position generally requires a Master's degree from an accredited college or university in computer science, computer engineering, artificial intelligence, machine learning, or a closely related discipline. A Ph.D. in one of these fields is strongly preferred. A Bachelor's degree in one of these fields, combined with seven or more years of directly relevant professional experience, will be considered in lieu of a Master's degree.QualificationsExperience and Foundational EngineeringAt minimum, three years of professional experience in machine learning or AI systems engineering, including at least one year building with large language models or other foundation models in a production settingStrong proficiency in Python, including asynchronous programming and modern packaging and dependency managementWorking knowledge of server-side development (API definitions, REST services, streaming and asynchronous services, etc.)Fluency with containerization and deployment frameworks such as Kubernetes or Docker, including GPU scheduling and resource management for training and inference workloadsHands-on work with at least one major cloud platform (AWS, Azure, or Google Cloud)Comfort with Linux platforms and command-line environmentsFamiliarity with Continuous Delivery/Continuous Integration (DevSecOps, GitLab Pipelines, etc.)Proficiency with automated testing in Python (pytest), including regression suites for non-deterministic model and agent behaviorArtificial Intelligence and Machine LearningDemonstrated ability to train and deploy machine learning models with PyTorch and the Hugging Face ecosystem (transformers, datasets, accelerate)A track record of building applications on top of large language models, including prompt engineering, structured output, and context managementFluency with agentic frameworks and patterns such as LangGraph, LangChain, CrewAI, AutoGen, Pydantic AI, or vendor agent SDKs, including multi-step tool use, planning, memory, and state management, and with integrating models, tools, and data sources through open standards such as Model Context Protocol (MCP)Practical command of retrieval-augmented generation, including chunking and embedding strategy, vector databases (pgvector, Milvus, Qdrant, Weaviate, FAISS), and hybrid or re-ranked retrievalAbility to design and run LLM evaluation, including task-specific benchmarks, golden datasets, LLM-as-judge methods, and tracing and observability tooling (LangSmith, Langfuse, Arize, Weights & Biases)Command of model adaptation techniques including fine-tuning, LoRA/PEFT, quantization, and distillation, and the judgment to know when adaptation is preferable to prompting or retrievalProven ability to serve models in production with inference frameworks such as vLLM, TensorRT-LLM, Triton Inference Server, Ollama, or ONNX Runtime, including latency, throughput, and cost tradeoffs, as well as deployment to edge or resource-constrained environments, including on-device inferenceGrounding in AI safety and assurance practices, including guardrails, input and output filtering, prompt injection mitigation, and human-in-the-loop designDirect work with multimodal models and cross-modal embedding across text, imagery, video, audio, or geospatial dataLeadership and CollaborationDemonstrated leadership on technical tasks and/or technical teams, including Agile software development and leading one or more tasks to completionExcellent communication and teamwork skills, including the ability to explain technical tradeoffs to non-technical stakeholders and customersNice to HaveExposure to DoD cloud and software factory environments such as Platform One, BESPIN, AF Cloud One, DAF CLOUDworks, or AWS GovCloud, and with ATO and RMF processes at IL4/IL5Familiarity with DoD and federal AI policy and governance, including CDAO Responsible AI guidanceWork with distributed training frameworks and schedulers (DeepSpeed, FSDP, Ray, Slurm)Knowledge of knowledge graphs, ontologies, or Resource Description Framework (RDF), particularly as applied to graph-based retrieval (GraphRAG) and groundingBackground in developing modern full-stack web applications with frameworks such as Node, React, React Native, or DjangoBasic working knowledge of Go, Java, C++, or other compiled languagesContributions to open source AI/ML projects, or published applied AI researchClearanceSelected applicants will undergo a security investigation and must meet and maintain eligibility for, at minimum, Top Secret access to classified information.BenefitsOur benefits package includes health, life, disability, dental, and vision insurance, plus a 401(k) plan with a 3% company contribution and 3% company match.Additional perks include:Generous Paid Time Off, including a PTO "gift day" for your birthday11 federal holidays per yearThree weeks of paid maternity/paternity leaveAnnual technology allowanceReferral bonuses and professional recognition awardsHealthcare stipendsTuition/education reimbursement (once eligibility requirements are met)Flexible daily start and stop times for most projects and positionsCompany DescriptionTechnergetics is a U.S.-based company headquartered in Utica, NY, with employees and clients located throughout the country. The Utica/Rome area is a hub of cutting-edge cyber technology research, bolstered by the Griffiss Business & Technology Park's tenants and facilities, including the Air Force Research Lab (AFRL). At Technergetics, we work with a wide variety of technologies, including mobile and web apps, quantum computing, machine learning and artificial intelligence, AI-enabled edge devices, and more. Beware of Fraudulent Job Offers and PostingsTechnergetics will never extend an offer of employment without a thorough interview process that includes a face-to-face interview - either in person or via a virtual Teams meeting - from an official Technergetics email address (@techngs.com). If you receive correspondence from any other email address, it is a scam. Technergetics does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
AI/ML Engineer IIITechnergetics - Utica/Rome, NY areaA Note About Our AI-Assisted Interview ProcessWe use an AI application, "Alex Taylor," to conduct first-round interviews for this position. The interview takes approximately 35 minutes. If we would like to interview you, you will receive an email invitation from "Alex" within ten business days of your application.Alex is available 24/7, which lets us conduct far more first-round interviews than our human staff's schedule alone would allow. Technergetics HR (and additional staff, as applicable) reviews every first-round interview. AI supports our decision-making, but all decisions about who advances to a first or second interview are made by our human staff. Candidates selected for a second-round interview will meet with the HR director and hiring manager.Any data collected during the interview process, including AI-generated insights, is handled with care and confidentiality, in compliance with applicable data protection laws. Your responses are processed only to provide feedback on your skills and knowledge. Data is stored securely and will not be shared with third parties without your consent.We understand some candidates may be hesitant to interview with an AI application - it is by no means perfect at this time. But as a company dedicated to research and development in AI/ML and other technologies, we see this as a chance to practice what we preach.Opportunity OverviewTechnergetics is looking for an AI/ML Engineer III to design, develop, and deploy advanced AI capabilities alongside a high-performing team of full-stack developers. This role centers on building production systems around foundation models, including agentic workflows, retrieval-augmented generation, and multimodal machine learning, for demanding government and commercial customers.Contingent Position: This position is contingent upon contract award and funding.Position DetailsSalary Range: $125,000-$175,000 annually. The final offer depends on how many position qualifications the candidate meets, as well as education and experience. This is a full-time, exempt position.Location, Travel, and Remote WorkCandidates who are located within, or relocate to, a commutable distance of the Utica/Rome area can expect to be onsite 20% of their workweek, for access to company and AFRL (Air Force Research Lab) facilities, secure data, and customers. A relocation signing bonus may be available.Remote candidates outside a commutable distance to Utica/Rome will still be considered but may need to travel to the Utica/Rome area quarterly or more often, depending on company and client needs.This position also involves approximately 5%-10% travel to customer and client sites outside the Utica-Rome, NY area.Due to the security clearance required for this position, only U.S. citizens are eligible to apply, per Executive Order 12968 (Access to Classified Information).Responsibilities and DutiesThe successful candidate will work on one or more of our Machine Learning (ML) software products, with day-to-day activities that include:Leading the design, development, and deployment of multi-modal machine learning architectures, including models and algorithms, to solve complex mission and business problemsDesigning and building agentic systems on top of large language models for operational deploymentImplementing retrieval-augmented generation pipelines that ground model outputs against authoritative data sourcesDefining and running evaluation for model and agent behavior in productionOptimizing model inference for production and edge/DDIL (denied, degraded, intermittent, and limited bandwidth) deployment scenariosApplying AI assurance practices and documenting model limitations to support accreditation and customer reviewIntegrating state-of-the-art machine learning libraries, foundation models, and agent frameworks into existing software applicationsDeveloping and maintaining data pipelines and supporting software for collecting, preprocessing, and transforming data for machine learning tasksDesigning software solutions, algorithms, and cloud architectures needed to satisfy product features and functionality defined by the product owner and other stakeholders in a production environmentLeading, coaching, and mentoring junior data scientists, engineers, and other staffContributing to phases of the software development life cycle, including functional analysis, technical requirements, technical design, prototyping, coding, testing, deployment, data migration, and supportParticipating in daily scrums and working with the scrum master and scrum team to organize and prioritize workload through story-pointing, supporting delivery timelines and prioritiesCollaborating with cross-functional teams to understand business requirements and translate them into machine learning solutionsPerforming unit testing and debugging to identify and fix software defects, and contributing to code reviews with constructive feedback to peersStaying current on new AI/ML approaches, frameworks, and industry trendsServing as an AI subject matter expert for small teams of researchers and engineers on advanced R&D projects funded by government and/or commercial customers, and contributing to or leading proposal writing for new opportunities within your area of expertiseEducation and CertificationsThis position generally requires a Master's degree from an accredited college or university in computer science, computer engineering, artificial intelligence, machine learning, or a closely related discipline. A Ph.D. in one of these fields is strongly preferred. A Bachelor's degree in one of these fields, combined with seven or more years of directly relevant professional experience, will be considered in lieu of a Master's degree.QualificationsExperience and Foundational EngineeringAt minimum, three years of professional experience in machine learning or AI systems engineering, including at least one year building with large language models or other foundation models in a production settingStrong proficiency in Python, including asynchronous programming and modern packaging and dependency managementWorking knowledge of server-side development (API definitions, REST services, streaming and asynchronous services, etc.)Fluency with containerization and deployment frameworks such as Kubernetes or Docker, including GPU scheduling and resource management for training and inference workloadsHands-on work with at least one major cloud platform (AWS, Azure, or Google Cloud)Comfort with Linux platforms and command-line environmentsFamiliarity with Continuous Delivery/Continuous Integration (DevSecOps, GitLab Pipelines, etc.)Proficiency with automated testing in Python (pytest), including regression suites for non-deterministic model and agent behaviorArtificial Intelligence and Machine LearningDemonstrated ability to train and deploy machine learning models with PyTorch and the Hugging Face ecosystem (transformers, datasets, accelerate)A track record of building applications on top of large language models, including prompt engineering, structured output, and context managementFluency with agentic frameworks and patterns such as LangGraph, LangChain, CrewAI, AutoGen, Pydantic AI, or vendor agent SDKs, including multi-step tool use, planning, memory, and state management, and with integrating models, tools, and data sources through open standards such as Model Context Protocol (MCP)Practical command of retrieval-augmented generation, including chunking and embedding strategy, vector databases (pgvector, Milvus, Qdrant, Weaviate, FAISS), and hybrid or re-ranked retrievalAbility to design and run LLM evaluation, including task-specific benchmarks, golden datasets, LLM-as-judge methods, and tracing and observability tooling (LangSmith, Langfuse, Arize, Weights & Biases)Command of model adaptation techniques including fine-tuning, LoRA/PEFT, quantization, and distillation, and the judgment to know when adaptation is preferable to prompting or retrievalProven ability to serve models in production with inference frameworks such as vLLM, TensorRT-LLM, Triton Inference Server, Ollama, or ONNX Runtime, including latency, throughput, and cost tradeoffs, as well as deployment to edge or resource-constrained environments, including on-device inferenceGrounding in AI safety and assurance practices, including guardrails, input and output filtering, prompt injection mitigation, and human-in-the-loop designDirect work with multimodal models and cross-modal embedding across text, imagery, video, audio, or geospatial dataLeadership and CollaborationDemonstrated leadership on technical tasks and/or technical teams, including Agile software development and leading one or more tasks to completionExcellent communication and teamwork skills, including the ability to explain technical tradeoffs to non-technical stakeholders and customersNice to HaveExposure to DoD cloud and software factory environments such as Platform One, BESPIN, AF Cloud One, DAF CLOUDworks, or AWS GovCloud, and with ATO and RMF processes at IL4/IL5Familiarity with DoD and federal AI policy and governance, including CDAO Responsible AI guidanceWork with distributed training frameworks and schedulers (DeepSpeed, FSDP, Ray, Slurm)Knowledge of knowledge graphs, ontologies, or Resource Description Framework (RDF), particularly as applied to graph-based retrieval (GraphRAG) and groundingBackground in developing modern full-stack web applications with frameworks such as Node, React, React Native, or DjangoBasic working knowledge of Go, Java, C++, or other compiled languagesContributions to open source AI/ML projects, or published applied AI researchClearanceSelected applicants will undergo a security investigation and must meet and maintain eligibility for, at minimum, Top Secret access to classified information.BenefitsOur benefits package includes health, life, disability, dental, and vision insurance, plus a 401(k) plan with a 3% company contribution and 3% company match.Additional perks include:Generous Paid Time Off, including a PTO "gift day" for your birthday11 federal holidays per yearThree weeks of paid maternity/paternity leaveAnnual technology allowanceReferral bonuses and professional recognition awardsHealthcare stipendsTuition/education reimbursement (once eligibility requirements are met)Flexible daily start and stop times for most projects and positionsCompany DescriptionTechnergetics is a U.S.-based company headquartered in Utica, NY, with employees and clients located throughout the country. The Utica/Rome area is a hub of cutting-edge cyber technology research, bolstered by the Griffiss Business & Technology Park's tenants and facilities, including the Air Force Research Lab (AFRL). At Technergetics, we work with a wide variety of technologies, including mobile and web apps, quantum computing, machine learning and artificial intelligence, AI-enabled edge devices, and more. Beware of Fraudulent Job Offers and PostingsTechnergetics will never extend an offer of employment without a thorough interview process that includes a face-to-face interview - either in person or via a virtual Teams meeting - from an official Technergetics email address (@techngs.com). If you receive correspondence from any other email address, it is a scam. Technergetics does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.