Lead MLOps Engineer Security Clearance

Position: Lead MLOps Engineer with Security Clearance
Drive the future of secure, scalable, mission-critical AI/ML systems.
Prime Solutions Group (PSG), Inc. is seeking a Lead MLOps Engineer to architect, automate, and operate advanced ML pipelines and platforms that power next-generation defense and national security AI systems. In this hybrid leadership role, you will serve as both a senior technical expert and a hands-on engineering lead—guiding MLOps strategy, mentoring engineers, and driving execution across high-impact AI/ML programs. You will integrate ML engineering, data engineering, and Dev Sec Ops  practices to build secure, scalable, fully automated ML ecosystems for both cloud and on-premise environments.

This role extends PSG’s Dev Sec Ops  foundation with ML-specific tooling and governance, including experiment tracking, model registries, monitoring, drift detection, automated retraining, and performance optimization. This is a high-visibility opportunity to deliver enterprise-scale AI/ML platforms and directly contribute to U.S. national security while shaping PSG’s long-term MLOps capabilities. Responsibilities Include
- 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 Dev Sec Ops  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 Dev Sec Ops  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. Requirements
- 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 - Dev Ops/Dev Sec Ops /SRE supporting ML workloads - Data engineering integrating ML pipelines - Applied ML in production environments
- Strong proficiency with CI/CD tools (Git Lab CI, Jenkins, Git Hub Actions, etc.).
- Hands-on experience with IaC (Terraform, Ansible, Cloud Formation).
- Expertise with Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).
- Strong experience with Python and ML frameworks (Num Py, pandas, scikit-learn, PyTorch, Tensor Flow).

- 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. Preferred Skills / Experience
- 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 Open Telemetry.

- Experience with :

- Feature stores - Data validation frameworks…