MLOps Engineer

Prime Solutions Group, Inc.•Goodyear, AZ

6d•$116,299

About The Position

Transform cutting-edge machine learning research into real-world mission capabilities. Prime Solutions Group (PSG) is seeking a highly capable MLOps Engineer to design, automate, and operate secure, scalable machine learning pipelines and infrastructure across enterprise and mission systems. In this role, you will work at the intersection of ML engineering, DevSecOps, cloud infrastructure, and cybersecurity—supporting advanced AI/ML workloads for defense and national security customers. You will collaborate closely with data scientists, DevSecOps engineers, and system architects to transition ML models into robust, production-ready services. This is a high-impact role supporting next-generation, AI-enabled digital engineering environments.

Requirements

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

Nice To Haves

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

Responsibilities

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.