Staff DevOps Engineer (AI/ML Ops)
- Serve as a technical authority for MLOps and ML lifecycle management across multiple programs.
- Design and implement ML-focused CI/CD pipelines that automate data builds, training, evaluation, packaging, and deployment.
- Embed ML-specific validation, security, and compliance controls into pipelines, including:
- Data quality and schema validation
- Model performance and inference gates
- Security scanning, compliance checks, and SBOM generation
- Build and maintain Infrastructure-as-Code (IaC) for ML platforms supporting:
- CPU/GPU training and inference clusters
- Data, feature, and model artifact storage
- Feature stores, model registries, and monitoring stacks
- Harden ML and data infrastructure in AWS cloud environments, aligning with NIST, FedRAMP, and CIS requirements.
- Mentor MLOps, data engineering, and software teams on secure, reliable, and observable ML operations.
- Partner with data scientists to move models from experimentation to production, enabling:
- Reproducible training environments
- Experiment tracking and model registry workflows
- Optimized, production-ready inference services
- Lead or support incident response and risk analysis related to ML systems (e.g., data quality issues, model drift, security exposures).
- Support audits and compliance efforts by documenting ML pipelines, controls, and evidence.
- Stay current on MLOps, DevSecOps, and ML platform trends, contributing to PSG’s AI/ML roadmap.
- U.S. Citizenship
- Active Top Secret clearance or higher
- Bachelor’s degree in Computer Science, Engineering, Data Science, Applied Mathematics, or related field
- CompTIA Security+ CE (or ability to obtain within 30 days of hire)
- 7+ years of experience in one or more of the following:
- MLOps or ML platform engineering
- DevOps / DevSecOps / SRE supporting data or ML workloads
- Data engineering with production ML integration
- Applied ML in production environments
- Strong experience building secure CI/CD pipelines for ML and data systems
- Proficiency in Python and scripting for automation
- Hands-on experience with AWS cloud infrastructure
- Experience with ML and data tools such as:
- ML frameworks (PyTorch, TensorFlow, scikit-learn)
- Data processing tools (pandas, Spark)
- Experiment tracking or model registries (MLflow, SageMaker, Weights & Biases)
- Solid understanding of compliance frameworks (NIST 800-53, FedRAMP, ISO 27001) as applied to ML and data systems
- Demonstrated experience mentoring or leading technical teams
- Strong software engineering fundamentals (Git workflows, code reviews, testing)
- Experience with containerization and orchestration (Docker, Kubernetes, Rancher)
- Familiarity with MLOps platforms and tooling:
- Workflow orchestration (Airflow, Kubeflow, Prefect, Dagster)
- Feature stores, data validation, and drift detection
- Experience with observability tools (Grafana, Splunk, Dynatrace, AppDynamics) for ML services
- Exposure to multi-cloud or hybrid ML environments
- Knowledge of Zero Trust security principles
- Experience in Agile/Scrum environments
- Experience building or integrating API-based ML services
- Exposure to Model-Based Systems Engineering (MBSE) environments
- Competitive compensation and benefits
- Professional development and tuition assistance
- A collaborative, mission-driven culture
- Direct impact on national security through secure AI/ML solutions
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