Senior Data Scientist / AI Engineer
Role details
Job location
Tech stack
Job description
AI/ML Solution Development
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Design, build, train, evaluate, and deploy machine learning and generative AI solutions.
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Develop and maintain predictive analytics, NLP, computer vision, and LLM-based applications.
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Implement Retrieval-Augmented Generation (RAG), agentic workflows, and knowledge management solutions.
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Evaluate commercial, open-source, and custom AI models for mission-specific use cases.
Local and On-Premises AI Infrastructure
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Deploy and operate local/open-source models in secure environments.
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Configure and optimize inference environments using GPUs and containerized deployments.
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Manage model serving platforms and inference frameworks.
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Implement monitoring, performance tuning, and lifecycle management for locally hosted models.
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Support disconnected, restricted, or air-gapped operational environments.
Azure Government AI Platforms
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Design and deploy AI solutions within Azure Government.
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Build and manage solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure Kubernetes Service (AKS), and related services.
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Implement secure model deployment, monitoring, and governance controls.
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Integrate AI services with enterprise systems and data platforms.
Data Engineering and Analytics
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Develop data pipelines supporting AI and analytics workloads.
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Perform data exploration, feature engineering, model evaluation, and performance analysis.
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Work with structured, semi-structured, and unstructured data sources.
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Ensure data quality, lineage, and governance standards are maintained.
MLOps and DevSecOps
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Implement CI/CD pipelines for machine learning and AI workloads.
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Develop automated testing, validation, and deployment processes.
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Establish model monitoring, drift detection, and performance reporting.
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Apply security controls and compliance requirements throughout the AI lifecycle.
Stakeholder Support
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Collaborate with mission owners, analysts, engineers, cybersecurity personnel, and leadership.
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Translate operational requirements into technical AI solutions.
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Prepare technical documentation, architecture diagrams, and presentations.
Requirements
We are seeking a Senior Data Scientist / AI Engineer to design, develop, deploy, and maintain machine learning and generative AI solutions within a government environment. This role will support both locally hosted AI systems and cloud-based AI services within Microsoft Azure Government, including Azure AI Foundry and related Azure AI services.
The ideal candidate has hands-on experience building production AI systems, deploying and operating open-source large language models (LLMs), implementing secure MLOps practices, and developing AI applications that meet government security and compliance requirements., + Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or related field.
- Master's degree preferred.
Professional Experience
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5+ years of experience in data science, machine learning, AI engineering, or related fields.
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2+ years of experience deploying and operating production AI/ML systems.
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Experience supporting secure government, defense, or regulated environments preferred.
Technical Skills
Machine Learning & Data Science
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Strong knowledge of supervised and unsupervised learning techniques.
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Experience with model development, evaluation, and optimization.
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Statistical analysis and experimental design experience.
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Proficiency in Python and common ML frameworks.
Generative AI & LLMs
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Experience deploying and operating open-source LLMs.
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Experience with:
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Llama family models
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Mistral models
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Hugging Face models
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Knowledge of:
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RAG architectures
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Agent frameworks
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Prompt engineering
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Model evaluation methodologies
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Fine-tuning approaches
Azure Government and Cloud AI
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Experience with:
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Azure AI Foundry
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Azure Machine Learning
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Azure OpenAI
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Azure Kubernetes Service (AKS)
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Azure Storage and Data Services
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Azure Identity and Access Management
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Experience deploying AI workloads in Azure Government environments preferred.
Local AI Infrastructure
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Experience with:
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Docker
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Kubernetes
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GPU-based inference systems
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vLLM, Ollama, TGI, or similar inference platforms
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Linux administration
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Understanding of model quantization and performance optimization techniques.
Data Platforms
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SQL and relational databases
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Data warehousing concepts
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ETL/ELT pipeline development
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Vector databases and semantic search platforms
Software Engineering
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Git-based development workflows
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REST APIs and microservices
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CI/CD pipelines
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Infrastructure-as-Code concepts
Preferred Qualifications
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Active security clearance or ability to obtain one.
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Experience with NIST AI Risk Management Framework.
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Experience with FedRAMP, RMF, or government cybersecurity compliance frameworks.
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Experience supporting classified or controlled environments.
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Azure certifications.
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Experience with distributed GPU environments.
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Experience implementing AI governance and responsible AI controls.
Desired Technologies
Candidates should have experience with several of the following:
Programming
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Python
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SQL
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PowerShell
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Bash
AI/ML Frameworks
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PyTorch
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TensorFlow
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Scikit-learn
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Hugging Face Transformers
LLM Ecosystem
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LangChain
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LlamaIndex
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Semantic Kernel
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OpenAI APIs
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Azure OpenAI APIs
Infrastructure
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Docker
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Kubernetes
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AKS
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Linux
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GitHub Actions
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Azure DevOps
Databases
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PostgreSQL
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SQL Server
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Vector databases
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Azure Data Services
Security Requirements
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U.S. citizenship required.
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Ability to pass government background investigation.
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Ability to comply with all applicable government security and information assurance requirements.
Benefits & conditions
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Health Care Plan (Medical, Dental & Vision)
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Retirement Plan (401k,)
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Life Insurance (Basic, Voluntary & AD&D)
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Paid Time Off (Vacation & Public Holidays)