AI / ML Engineer

Role overview This role advances U.S. federal government missions by designing, building, and operationalizing applied AI and machine learning systems. The engineer works closely with program stakeholders to convert mission needs into working technical solutions that span generative AI, classical ML, and cloud data engineering. It blends hands-on model development with technical leadership, guiding teams toward dependable production AI.

Responsibilities - Collaborate with stakeholders to surface high-value AI/ML use cases and shape them into deployable solutions - Design, train, fine-tune, and evaluate ML and generative AI systems including LLMs, retrieval-augmented generation, embeddings, and deep learning models - Construct end-to-end ML pipelines covering ingestion, feature engineering, orchestration, and CI/CD for models and prompts - Deploy scalable models and agents, then operate them responsibly with monitoring, drift detection, and incident response - Work alongside data engineering to shape data architecture using warehouse, streaming, and feature store services - Apply Responsible AI, security, governance, and compliance practices throughout the model lifecycle

Requirements - U.S. citizenship with eligibility for a Public Trust clearance - 3–6+ years in machine learning engineering, data science, or applied AI development - 3+ years leading technical teams and shaping technical standards in cloud or on-prem environments - Strong Python and SQL fluency plus experience with TensorFlow, PyTorch, or scikit-learn - Hands-on work with cloud AI platforms such as Vertex AI and Gemini APIs - Working knowledge of LLMs, embeddings, vector search, and generative AI techniques

Nice to have - Master's degree and prior federal or regulated industry experience with FedRAMP, HIPAA, or NIST frameworks - Background in Responsible AI, bias mitigation, or model interpretability - Familiarity with operational cloud tooling such as IAM, KMS, logging and monitoring, networking, and storage - Exposure to alternative cloud platforms or third-party observability and DevOps tooling