Lead MLOps Engineer
- Lead the design, implementation, and management of ML-focused CI/CD pipelines across development, test, staging, and production environments.
- Integrate MLOps best practices into existing DevSecOps workflows, including:
- Data quality and schema validation
- Model validation and promotion gates
- Drift and performance monitoring
- Oversee secure Infrastructure-as-Code (IaC), containerization (Docker/Kubernetes), and cloud platforms (AWS/Azure/GCP) for ML and data workloads.
- Architect and maintain ML training and inference platforms, including experiment tracking, model registries, and automated retraining pipelines.
- Mentor and guide engineers in automation, observability, and security-first MLOps and DevSecOps practices.
- Collaborate with cross-functional teams (data science, software, cybersecurity, IT, systems) to ensure ML systems are reliable, secure, and high-performing.
- Lead technical risk assessments and incident response efforts for ML and data platforms.
- Stay current on emerging MLOps, data engineering, and AI platform technologies; recommend new tools and methods.
- Serve in a hybrid role as:
- Senior technical contributor on MLOps architecture and implementation
- Team lead for MLOps initiatives and platform development efforts
- Contribute hands-on to pipeline/orchestration code, infrastructure definitions, and monitoring/alerting configuration.
- Apply engineering principles to resolve complex issues across ML, data, security, and operations.
- Evaluate ethical, operational, and mission considerations when deploying AI/ML systems.
- U.S. Citizenship (Required).
- Active Top-Secret Clearance or higher.
- Bachelor’s degree in Computer Science, Engineering, Data Science, Applied Mathematics, or related field.
- 5–9+ years experience in:
- MLOps / ML platform engineering
- DevOps/DevSecOps/SRE supporting ML workloads
- Data engineering integrating ML pipelines
- Applied ML in production environments
- Strong proficiency with CI/CD tools (GitLab CI, Jenkins, GitHub Actions, etc.).
- Hands-on experience with IaC (Terraform, Ansible, CloudFormation).
- Expertise with Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).
- Strong experience with Python and ML frameworks (NumPy, pandas, scikit-learn, PyTorch, TensorFlow).
- Experience with orchestration tools (Airflow, Kubeflow, Prefect, Dagster).
- Experience integrating security scanning, governance, and compliance frameworks into ML workflows.
- Strong scripting skills (Python, Bash, Go, or similar).
- Demonstrated leadership experience—technical mentorship, leading projects, or team oversight.
- Excellent communication skills with the ability to convey ML system behavior and trade-offs to diverse stakeholders.
- Master’s degree in a relevant field.
- Additional security or cloud certifications (CISSP, AWS ML Specialty, CKA/CKS, etc.).
- Experience implementing Zero Trust, advanced observability (Prometheus, Grafana, ELK/EFK), or OpenTelemetry.
- Experience with:
- Feature stores
- Data validation frameworks (Great Expectations)
- Data governance and lineage tooling
- Policy-as-code (OPA, Kyverno)
- Prior experience supporting defense, aerospace, or government-secured AI/ML programs.
- Experience designing/operating mission-critical AI/ML systems with high throughput, high availability, and rigorous monitoring.
- Competitive compensation & benefits
- Professional development & tuition assistance
- Collaborative, mission-driven culture
- Direct impact on high-visibility AI/ML government programs
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