AI/ML Engineer

Key Responsibilities:

• Actively participate in whole AI/ML pipeline design, development, and implementation lifecycle.
• Conduct exploratory data analysis (EDA), data cleaning, and statistical validation aligned with DoD data assurance principles.
• Engineer feature pipelines and automate feature stores in Azure Feature Store or within Databricks.
• Build end-to-end ML pipelines with MLflow, Delta Lake, and Azure Machine Learning (AML) for training, evaluation, and model lifecycle management.
• Provide guidance on implementing AI agent frameworks such as LlamaIndex, crewAI, LangGraph, and Azure AI Foundry.
• Design, implement, and optimize AI solutions leveraging both Large Language Models (LLMs) and Small Language Models (SLMs), ensuring scalability, efficiency, and alignment with business objectives.
• Develop and deploy scalable Machine Learning models using Azure Databricks and Lakehouse architecture.
• Enforce security and data governance via Unity Catalog, role-based access control (RBAC), and Key Vault integration.
• Collaborate with data scientists, DevSecOps engineers, and cybersecurity SMEs to ensure secure data processing, model deployment and operationalize the deployed models.
• Support model bias detection, adversarial robustness, and interpretability.
• Collaborate on ATO documentation and contribute to ML-specific security artifacts and POA&Ms.
  

Mandatory Skills & Qualifications:

• Must be legally eligible for employment in the U.S.
• Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or related field
• 5 or more years of hands-on experience in data engineering (preferably in cloud environment) with 3 or more years of experience in Machine Learning engineering roles, preferably in secure or classified environments
• Strong programming skills in Python, SQL, PySpark, Jupyter notebooks, and distributed computing
• Experienced in applying or fine-tuning foundational models or large language models (LLM) in mission critical settings.
• Demonstrated experience in designing and integrating diverse data sources to support Agentic AI systems, including structured, unstructured, and real-time data pipelines in production environments
• Proficiency with Retrieval-Augmented Generation (RAG) frameworks, including implementation of vector databases, embedding models, and retrieval mechanisms to enhance contextual relevance and model performance
• Expert level proficiency in
• Azure Databricks, PySpark, scikit-learn
• MLflow for model tracking and deployment
• Azure ML and associated MLOps tools
• Experienced in deploying ML models securely in cloud-native architectures
• Knowledge of secure data handling, encryption, and role-based access in Azure
• Proficiency in deploying microservices and models APIs using Docker, Cloud Run, or Kubernetes (EKS or GKE)
• Ability to integrate structured, semi-structured, and unstructured data from APIs, RDBMS, and/or streaming sources into Snowflake or Azure SQL DW.
  

Preferred Skills & Qualifications:

• Unity Catalog, Delta Live Tables, Databricks Repos
• Deep Learning frameworks like TensorFlow, PyTorch
• CI/CD for AI/ML (MLOps) using Azure DevOps or GitHub Actions
• Familiarity with DoD data strategy, RMF, NIST 800-53, CMMC, and/or FedRAMP
• Experience in handling classified data, synthetic data generation, or mission-critical AI/ML systems
• Experience working in Azure Government, IL5/6, or JWCC environments
• Certification such as:
• DP-100: Designing and Implementing a Data Science Solution on Azure
• AZ-305: Azure Solutions Architect
• DP-700: Fabric Data Engineer Associate