Senior MLOps Engineer

Prime Solutions Group, Inc.•Goodyear, AZ

3d•$110,000

About The Position

PSG is seeking a Senior MLOps Engineer to lead the design, deployment, and optimization of machine learning platforms and pipelines supporting mission-critical systems. In this role, you’ll enable AI/ML capabilities to move seamlessly from development to production by building secure, scalable, and automated infrastructure. You’ll work at the intersection of MLOps, DevSecOps, and cloud engineering, partnering with ML engineers, software developers, and security teams to deliver reliable, compliant, and high-performing AI systems in complex environments.

Requirements

U.S. Citizenship
Active Top Secret Clearance (SCI eligibility; CI Poly preferred or ability to obtain)
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
4+ years of experience in:
MLOps or ML platform engineering
DevOps/DevSecOps supporting ML workloads
Cloud infrastructure engineering for data/ML systems
Experience with CI/CD tools (GitLab CI, Jenkins, GitHub Actions)
Proficiency with Infrastructure-as-Code (Terraform, Ansible, or similar)
Experience with Docker and Kubernetes
Hands-on experience with AWS, Azure, or GCP (including GPU-based workloads)
Strong understanding of the ML lifecycle (training ? deployment ? monitoring ? retraining)
Scripting experience (Python, Bash, or PowerShell)
Familiarity with security best practices in regulated environments
Strong problem-solving, communication, and collaboration skills

Nice To Haves

Master’s degree in a related field
Experience with ML orchestration and tooling (Airflow, Kubeflow, MLflow, etc.)
Experience with model registries, experiment tracking, and reproducibility frameworks
Familiarity with RMF, STIGs, or other compliance frameworks
Experience with observability tools (Prometheus, Grafana, ELK, etc.)
Background supporting AI/ML systems in defense, intelligence, or high-assurance environments
Relevant certifications (AWS DevOps Engineer, AWS ML Specialty, Kubernetes, etc.)

Responsibilities

Architect and maintain scalable ML pipelines for training, evaluation, deployment, and retraining
Build and manage CI/CD pipelines for ML workflows, including automated testing and release controls
Deploy and support containerized ML services using Docker, Kubernetes, and cloud platforms
Implement Infrastructure-as-Code (IaC) for compute, storage, networking, and GPU-enabled environments
Establish reproducibility across datasets, models, code, and runtime environments
Integrate monitoring and observability for ML systems (latency, throughput, resource usage, model performance)
Partner with ML and software teams to transition models into production environments
Ensure ML platforms meet security and compliance standards (IAM, secrets management, system hardening)
Troubleshoot complex pipeline, infrastructure, and deployment issues; drive root cause resolution
Evaluate and implement modern MLOps tools, frameworks, and best practices
Develop technical documentation, architecture diagrams, and operational runbooks
Mentor junior engineers and contribute to architecture reviews and technical planning