AI/ML Engineer

Design, build, and operate AI/ML solutions on the Advana data and analytics platform, including traditional ML and Generative AI/LLM capabilities. Work closely with data engineers, software engineers, and mission stakeholders to turn data into deployed, monitored models that support DoD decision-making.

Core Responsibilities

Develop, train, and evaluate ML models (classification, regression, clustering, NLP, time series). Build MLOps pipelines for data prep, training, deployment, and monitoring on cloud infrastructure. Integrate models into production services and user-facing applications (APIs, microservices, dashboards). Implement and tune Generative AI/LLM solutions (e.g., RAG, prompt engineering, fine-tuning) using enterprise and mission data. Collaborate with data engineers to define features, data quality checks, and scalable data pipelines. Monitor model performance and drift; design retraining strategies and A/B tests. Document models, assumptions, and limitations; support DoD Responsible/Ethical AI requirements.

Clearance: U.S. Citizenship required; active Secret/TS/SCI preferred; ability to obtain and maintain clearance Level: Junior / Journeyman / Senior, Strong Python development (pandas, NumPy, scikit-learn; plus TensorFlow and/or PyTorch). Hands-on experience building and deploying ML models end-to-end (from data exploration to production). Practical experience with MLOps tools and patterns (e.g., MLflow, SageMaker, Kubeflow, or similar). Solid understanding of statistics, ML fundamentals, and evaluation metrics. Experience working with cloud services (preferably AWS: S3, Lambda, ECR, ECS/EKS, SageMaker or equivalents). Proficient with SQL and working with large structured/unstructured datasets. Ability to work with cross-functional teams (data, software, product, mission). Preferred Qualifications

Experience with LLMs / Generative AI, RAG architectures, and vector databases. Experience on large-scale data platforms (Databricks, Spark) and event/stream processing. Experience in DoD, Federal, or other highly regulated environments (security, compliance, RMF awareness). Familiarity with CI/CD, containerization (Docker), and Kubernetes for model deployment. Relevant certifications (e.g., AWS Machine Learning Specialty, Databricks, or similar).