DevOps Engineer (AI/ML Ops)
- Design, build, and maintain secure, automated ML pipelines for data ingestion, feature engineering, model training, validation, and deployment.
- Implement ML-aware CI/CD pipelines with unit tests, data validation, model validation, and promotion gates aligned to DevSecOps best practices.
- Automate model training, evaluation, and deployment using orchestration platforms (Airflow, Kubeflow, Prefect, Dagster, etc.) and model registries/experiment tracking tools.
- Containerize and deploy ML services (REST/gRPC microservices, batch, or streaming inference) using Docker and Kubernetes.
- Integrate monitoring, drift detection, and data quality checks into ML production systems.
- Partner with data scientists to transition models from experimentation to production, ensuring reproducibility and consistent environments.
- Collaborate with DevSecOps, infrastructure, and security teams to meet PSG security baselines (image scanning, SBOMs, secrets management, IAM).
- Monitor and optimize ML training and inference performance, including GPU/CPU utilization and cloud cost efficiency.
- Troubleshoot complex issues across data pipelines, model services, cloud infrastructure, and ML orchestration tools.
- U.S. Citizenship (required)
- Active Top Secret Clearance or higher
- Bachelor’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, or related field
- 2–4+ years of experience in at least one of the following:
- MLOps or ML platform engineering
- DevOps/DevSecOps/SRE for ML workloads
- Data engineering with ML integration
- Applied ML in production environments
- Proficiency with Git and CI/CD tools (GitLab CI, Jenkins, GitHub Actions, etc.)
- Hands-on experience with AWS, Azure, or GCP ML infrastructure
- Strong Python skills and experience with ML libraries (NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
- Experience with Docker and Kubernetes
- Strong understanding of the ML lifecycle (feature engineering ? training ? validation ? deployment ? monitoring ? retraining)
- Clear communication and cross-functional collaboration skills
- Experience operating ML systems in production
- Hands-on experience with:
- MLflow, Weights & Biases, or similar model registries
- Airflow, Kubeflow, Prefect, Dagster, or similar orchestrators
- Feature stores or scalable data pipelines
- Experience integrating security into ML workflows (image/dependency scanning, policy-as-code)
- Familiarity with observability stacks (Prometheus, Grafana, EFK, OpenTelemetry) and ML-specific monitoring
- Knowledge of Zero Trust Architecture, NIST frameworks, and DoD STIG compliance
- Certifications: AWS ML Specialty, AWS DevOps, CKS, or related
- Experience supporting mission-critical AI/ML systems for defense, intelligence, or critical infrastructure
- Competitive compensation & benefits
- Professional development & tuition assistance
- A collaborative, mission-driven culture
- A small-company environment where innovation happens fast
- Direct impact on high-visibility government programs leveraging AI/ML
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