MLOps Engineer

Role Overview

MLOps Engineer designing, implementing, and supporting MLOps platforms and operational processes to enable scalable, secure, and reliable deployment of machine learning solutions for federal clients.

Responsibilities

• Design, build, and maintain end-to-end MLOps pipelines (training, testing, deployment, monitoring, retraining)
• Implement CI/CD workflows for ML models and data pipelines in secure federal environments
• Operationalize models from data science teams to ensure production readiness
• Develop and manage model versioning, artifact management, and experiment tracking
• Build monitoring for model performance, model drift, data quality, and pipeline health
• Automate infrastructure provisioning and deployment using infrastructure-as-code and DevOps best practices
• Support auditability, explainability, and governance for AI/ML systems
• Partner with data scientists/AI engineers/data engineers and government stakeholders to align architecture with mission needs and security requirements

Requirements

• US citizenship required
• Active Secret (and maintained) federal/DoD security clearance
• Bachelor’s degree
• 3–5 years experience in MLOps, ML engineering, data engineering, DevOps, or related roles
• Strong Python experience for ML tooling related to packaging, deployment, and monitoring
• Hands-on CI/CD experience for ML/data workloads
• Experience with containerization and orchestration (e.g., Docker, Kubernetes)
• Experience working with secure cloud or hybrid environments for federal/DoD clients
• Knowledge of ML lifecycle concepts: versioning, reproducibility, monitoring
• Ability to communicate complex designs across technical and non-technical teams

Nice to Have

• Experience supporting Department of Defense, including Advana or enterprise analytics platforms
• Experience operationalizing ML solutions for federal financial/budgetary data
• Hands-on with Databricks (including MLflow, Spark, Delta Lake)
• Experience deploying/maintaining ML workflows in Palantir Foundry
• Familiarity with governance/compliance/risk controls for AI in federal environments
• Experience with Azure (Azure Government) or AWS GovCloud
• Knowledge of responsible AI, model risk management, or regulated ML environments
• Master’s degree in a relevant technical field