Job description

About the position 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. Requirements • 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. Nice-to-haves • Knowledge of 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. Benefits • Medical, dental & vision. • Critical Illness, Accident, and Hospital. • 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available. • Life Insurance (Voluntary Life & AD&D for the employee and dependents). • Short and long-term disability. • Health Spending Account (HSA). • Transportation benefits. • Employee Assistance Program. • Time Off/Leave (PTO, Vacation or Sick Leave).

Requirements