Lead MLOps Engineer

Prime Solutions Group, Inc.•Goodyear, AZ

3d•$138,337

About The Position

Prime Solutions Group (PSG), Inc. is seeking a Lead MLOps Engineer to serve as a hybrid senior technical contributor and team leader, responsible for designing, implementing, and operating secure, automated machine learning and data pipelines across cloud and on-premise environments. In this role, you will sit at the intersection of machine learning, data engineering, and DevSecOps, ensuring ML models and data-driven services are scalable, secure, observable, and compliant across their full lifecycle—from data ingestion and feature engineering through training, deployment, monitoring, and retraining. You will guide technical execution, mentor engineers, and make key architectural and tooling decisions for PSG’s MLOps platforms. Building on PSG’s established DevSecOps foundation (CI/CD, Infrastructure-as-Code, security baselines), you will extend capabilities to include experiment tracking, model registries, drift detection, and model performance monitoring. This is a fast-paced, high-impact opportunity to deliver enterprise-scale AI/ML solutions while directly supporting U.S. national security missions.

Requirements

U.S. Citizenship
Active Top Secret clearance or higher
Bachelor’s degree in Computer Science, Engineering, Data Science, Applied Mathematics, or related field
5–9+ years of experience in one or more of the following: MLOps or ML platform engineering DevOps / DevSecOps / SRE supporting data or ML workloads Data engineering with production ML integration Applied machine learning in production environments
Strong experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions, CircleCI) and modern Git workflows
Hands-on experience with Infrastructure-as-Code (Terraform, Ansible, CloudFormation) and Kubernetes
Proficiency with ML and data technologies, including: Python and ML/data libraries (NumPy, pandas, scikit-learn, PyTorch, TensorFlow) Workflow/orchestration tools (Airflow, Kubeflow, Prefect, Dagster) Experiment tracking and model registries (MLflow, Weights & Biases, SageMaker)
Experience integrating security and governance into ML environments (image/dependency scanning, SBOMs, secrets management, IAM)
Familiarity with NIST, FedRAMP, and DoD RMF compliance frameworks as applied to ML and data systems
Strong scripting or programming skills (Python, Bash, Go, or similar)
Demonstrated experience leading technical efforts and mentoring engineers
Ability to communicate clearly with both technical and non-technical stakeholders

Nice To Haves

Security, cloud, or ML certifications (e.g., CISSP, AWS Security Specialty, AWS ML Specialty, CKS, GIAC)
Experience implementing Zero Trust architectures
Experience with observability and monitoring tools (Prometheus, Grafana, ELK/EFK, OpenTelemetry) for ML services
Hands-on experience with: Feature stores and data validation frameworks (e.g., Great Expectations) Data governance and lineage tooling Policy-as-code for ML environments (OPA, Kyverno, admission controllers)
Prior experience supporting defense, aerospace, or government-secured AI/ML programs
Experience operating enterprise-scale or mission-critical ML systems, including high-availability inference and rigorous performance monitoring

Responsibilities

Lead the design, implementation, and operation of ML-focused CI/CD pipelines supporting data ingestion, feature engineering, model training, evaluation, and deployment across dev, test, staging, and production environments.
Apply and adapt MLOps best practices within existing DevSecOps workflows, including: Data quality checks and schema validation Model validation and promotion gates Model performance and drift monitoring
Architect and oversee training and inference platforms, including experiment tracking, model registries, and automated retraining pipelines.
Oversee secure integration of Infrastructure-as-Code, containerization, and orchestration (Docker, Kubernetes) for ML and data workloads, including GPU and high-performance compute resources.
Mentor and guide engineers in MLOps and DevSecOps practices, promoting automation, observability, and security-first design.
Collaborate with cross-functional teams (data science, software engineering, research, IT, cybersecurity, systems engineering) to ensure ML system reliability, performance, and compliance.
Lead technical risk assessments and contribute to incident response for ML and data systems (e.g., model degradation, data quality issues, pipeline failures).
Serve in a hybrid role as both: A senior hands-on engineer contributing to pipelines, infrastructure, and monitoring A technical leader guiding small to mid-sized MLOps initiatives
Make informed technical decisions across ML, data, security, and operations domains, resolving complex multi-disciplinary challenges.
Evaluate ethical and operational considerations in AI/ML deployment (e.g., bias, data constraints, mission risk) and recommend appropriate mitigations.
Stay current on emerging MLOps, AI platform, and data engineering technologies, recommending adoption where beneficial.