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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Amentum Services, Inc. - **Location:** Baltimore, MD, United States - **Experience:** Experienced - **Salary:** $195,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Clusters, Continuous Integration, Data Cleansing, Information Engineering, Software Design Patterns, Python (Programming Language), Machine Learning, Language Modeling, Parsing, Azure Machine Learning, Software Engineering, Tokenization, Cloud Collaboration, QLoRA, Data Ingestion, LangChain, Office365, Retrieval-Augmented Generation, Large Language Models, IT Architecture, Qwen, Model Validation, Llamaindex, Generative AI, AI Platforms, Information Technology, Amazon Bedrock, Machine Learning Operations, Hardware Infrastructure, Drift Detection, Api Design, Model Context Protocol, Semantic Kernel, Software Version Control, Docker - **Published:** October 14, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f0a61d431df0ab41 ## About the Role * Demonstrated experience designing, building, and deploying at least one RAG application for real users, covering document ingestion, chunking, embedding selection, vector store configuration, and retrieval evaluation. * Demonstrated experience fine-tuning or adapting open-weight LLMs (LoRA, QLoRA, or full supervised fine-tuning) and measuring results against a defined benchmark. * Experience hosting and serving models directly on GPU infrastructure (vLLM, TGI, NVIDIA Triton, or similar), including GPU sizing and quantization tradeoffs. * Experience delivering AI capabilities in disconnected or classified environments, including moving models, containers, and dependencies across the air gap and supporting the system through ATO. * Experience building access controls retrieval pipelines so each user retrieves only content that matches their clearance, compartments, and need-to-know. * Experience building evaluation and guardrails for LLM output, including accuracy testing, hallucination measurement, red teaming, and prompt injection defense. * Experience building document ingestion pipelines for large, unstructured collections, including parsing, OCR, metadata tagging, and preserving classification markings. * Proficiency in Python and at least one LLM orchestration framework (LangChain, LlamaIndex, Semantic Kernel, or similar), plus containerization with Docker and Kubernetes. * Ability to explain AI concepts, tradeoffs, and limitations clearly to technical and nontechnical audiences, including Government leadership. Minimum Abilities * Ability to translate mission requirements into working AI architecture and choose the right approach (prompting, RAG, fine-tuning, or agents) for each problem. * Ability to evaluate emerging models and tools quickly and recommend which merit adoption within customer security constraints. * Ability to lead technical work across engineering, security, and mission teams. * Ability to balance delivery speed with customer security, compliance, and governance requirements. Security Clearance Required: * Active TS/SCI with Polygraph. Minimum Education: * Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field. * We consider an equivalent combination of education and relevant experience in place of a degree. Minimum Years of Experience: * ing aMinimum 5 years of professional experience in software engineering, machine learning engineering, or data engineering. * At least 2 years designing, building, and deploying LLM or machine learnpplications that real operational or business users rely on. Preferred Qualifications: * Prior roles as an AI Engineer, Machine Learning Engineer, Software Engineer, Data Engineer, or Solution Architect with direct build responsibility. * Experience supporting Intelligence Community, DoD, or Federal customers. * Experience deploying AI workloads in classified cloud regions (AWS Secret or Top Secret, Azure Government Secret or Top Secret) or on GPU clusters the Government owns and operates. * Experience building agentic workflows that call tools, APIs, or other systems, including Model Context Protocol (MCP) or similar integration standards. * Experience with MLOps or LLMOps practices, including model versioning, CI/CD for models, monitoring, and drift detection. * Experience preparing or contributing to ATO packages for systems that include AI or machine learning components. * Experience with enterprise AI platforms such as Microsoft 365 Copilot, Azure AI Services, or Amazon Bedrock in Government cloud regions. * Experience coaching teams on effective AI use and building business cases that quantify the productivity gains from AI adoption. ## Description Responsible for designing, building, and deploying versatile artificial intelligence systems by integrating machine learning models into production software. They bridge the gap between data science and traditional software engineering, handling everything from data preprocessing and model selection to API development and cloud infrastructure scalability. Work Environment, Physical Demands, and Mental Demands: Hybrid or modern office environment equipped with high-performance computing infrastructure and advanced cloud collaboration tools. Their day-to-day involves cross-functional collaboration, straddling the line between technical development teams and corporate executives to align data strategies with business goals. The environment can be fast-paced and high-pressure, requiring constant adaptation to evolving technologies. Minimum Knowledge * Large language model fundamentals, including transformer architecture, tokenization, embeddings, context windows, inference parameters, and common failure modes such as hallucination and prompt injection. * Retrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation. * Model adaptation methods, including supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, along with when each approach outperforms prompting or RAG alone. * The open-weight model ecosystem (Llama, Mistral, Qwen, Gemma, or similar) and the licensing, provenance, and supply chain considerations for bringing models into Government environments. * Security and compliance frameworks that govern AI in classified environments, including RMF and the ATO process, ICD 503, the NIST AI Risk Management Framework, and handling requirements for CUI and classified data.