Senior MLOps Engineer

Prime Solutions Group, Inc.•Goodyear, AZ

6d•$138,337

About The Position

Shape the future of AI/ML for national security. Prime Solutions Group (PSG), Inc. is seeking a Senior MLOps Engineer to lead the development of secure, scalable, and automated ML platforms powering mission-critical AI/ML programs. In this high-impact role, you will architect and operate end-to-end ML pipelines across classified and unclassified environments, enabling next-generation AI capabilities for defense and advanced sensing systems. As a senior technical leader, you will mentor junior engineers, guide architecture decisions, and serve as a subject matter expert across MLOps, ML infrastructure, DevSecOps alignment, and secure ML deployment. You will work closely with data scientists, software engineers, and security teams to operationalize ML models into reliable, observable, and compliant production systems. This position is ideal for an experienced MLOps engineer who thrives in a fast-paced environment, is passionate about enterprise-scale AI/ML systems, and wants to directly impact U.S. national security.

Requirements

U.S. Citizenship (required).
Active Top-Secret Clearance (or higher)
Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or related field.
4–6+ years of experience in at least one of the following: MLOps / ML platform engineering DevOps / DevSecOps / SRE for ML workloads Data engineering with production ML workflows Applied ML in production environments
Strong experience with secure CI/CD pipelines and IaC (GitLab CI, Jenkins, GitHub Actions, Terraform, Ansible).
Hands-on expertise with Docker, Kubernetes, and at least one major cloud provider (AWS/Azure/GCP), including GPU/HPC support.
Strong understanding of the full ML lifecycle (data ? features ? training ? validation ? deployment ? monitoring ? retraining).
Proficiency with Python and standard ML/data libraries (NumPy, pandas, scikit-learn, PyTorch, TensorFlow).
Strong scripting skills (Python, Bash, PowerShell) for automation.
Familiarity with RMF, STIGs, DISA, and secure ML deployment practices.
Ability to lead projects, make architecture decisions, and mentor technical staff.
Excellent communication and documentation skills.

Nice To Haves

Master’s degree in a related field.
Active Security Clearance above minimum requirements (SCI, CI Poly).
Industry certifications: AWS ML Specialty, AWS DevOps, CKA/CKS, etc.
Experience with: MLflow, Weights & Biases, SageMaker, or similar registries/experiment tracking Orchestration frameworks (Airflow, Kubeflow, Prefect, Dagster) Feature stores and data validation tools (Great Expectations, Feast)
Experience with Zero Trust, SBOMs, and secure software supply chain principles.
Familiarity with NIST 800-53, FedRAMP, and ISO 27001 as they relate to ML/AI systems.
Kubernetes security expertise (RBAC, network policies, hardened images).
Background supporting defense, intelligence, or other high-assurance environments.

Responsibilities

Design, build, and maintain ML-focused CI/CD pipelines with automated testing, security checks, and model validation gates.
Architect and implement data ingestion, ETL/ELT, and feature engineering pipelines using modern data engineering frameworks.
Lead development of training, evaluation, and retraining workflows with experiment tracking and model registry integration.
Containerize and deploy ML models (REST/gRPC microservices, batch jobs, and streaming inference) using Docker and Kubernetes across cloud and on-prem environments.
Implement Infrastructure-as-Code (IaC) using Terraform, Ansible, or similar tools for provisioning compute, storage, networking, and GPU resources.
Integrate data quality checks, drift detection, and model performance monitoring into production ML systems.
Ensure ML workloads comply with NIST, RMF, FedRAMP, and PSG security baselines (image scanning, SBOMs, secrets management, hardening).
Partner with data scientists and software engineers to move models from experimentation to production, including packaging, dependency management, and optimization.
Monitor ML infrastructure using Prometheus/Grafana, ELK/EFK, or similar observability stacks; lead incident root-cause analysis.
Independently lead projects, influence architecture decisions, and navigate tool selection for enterprise ML platforms.
Integrate ML-specific security and quality testing into workflows (SAST/DAST, container security scanning, policy-as-code).
Develop technical documentation, runbooks, diagrams, and risk assessments for ML platforms.
Mentor junior staff and provide guidance on architecture, pipelines, code quality, and operational best practices.
Participate in architecture reviews, compliance assessments, and configuration management processes.