Staff MLOps Engineer

Prime Solutions Group, Inc.•Goodyear, AZ

6d•$167,967

About The Position

Build, scale, and secure mission-critical AI/ML systems that matter. Prime Solutions Group (PSG), Inc. is seeking a Staff MLOps Engineer to lead how machine learning models and data-driven services are built, deployed, secured, and operated across multiple programs. This is a senior technical leadership role for an engineer who sets direction beyond individual projects, collaborates across disciplines, and operates with minimal supervision. Building on PSG’s mature DevSecOps foundation, you will define and drive enterprise MLOps best practices spanning the full ML lifecycle—from data ingestion and feature engineering through deployment, monitoring, drift detection, and retraining. This is a fast-paced, high-impact opportunity to deliver secure, scalable AI/ML solutions while mentoring technical teams and 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
CompTIA Security+ CE (or ability to obtain within 30 days of hire)
7+ 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 ML in production environments
Strong experience building secure CI/CD pipelines for ML and data systems
Proficiency in Python and scripting for automation
Hands-on experience with AWS cloud infrastructure
Experience with ML and data tools such as: ML frameworks (PyTorch, TensorFlow, scikit-learn) Data processing tools (pandas, Spark) Experiment tracking or model registries (MLflow, SageMaker, Weights & Biases)
Solid understanding of compliance frameworks (NIST 800-53, FedRAMP, ISO 27001) as applied to ML and data systems
Demonstrated experience mentoring or leading technical teams
Strong software engineering fundamentals (Git workflows, code reviews, testing)

Nice To Haves

Experience with containerization and orchestration (Docker, Kubernetes, Rancher)
Familiarity with MLOps platforms and tooling: Workflow orchestration (Airflow, Kubeflow, Prefect, Dagster) Feature stores, data validation, and drift detection
Experience with observability tools (Grafana, Splunk, Dynatrace, AppDynamics) for ML services
Exposure to multi-cloud or hybrid ML environments
Knowledge of Zero Trust security principles
Experience in Agile/Scrum environments
Experience building or integrating API-based ML services
Exposure to Model-Based Systems Engineering (MBSE) environments

Responsibilities

Serve as a technical authority for MLOps and ML lifecycle management across multiple programs.
Design and implement ML-focused CI/CD pipelines that automate data builds, training, evaluation, packaging, and deployment.
Embed ML-specific validation, security, and compliance controls into pipelines, including: Data quality and schema validation Model performance and inference gates Security scanning, compliance checks, and SBOM generation
Build and maintain Infrastructure-as-Code (IaC) for ML platforms supporting: CPU/GPU training and inference clusters Data, feature, and model artifact storage Feature stores, model registries, and monitoring stacks
Harden ML and data infrastructure in AWS cloud environments, aligning with NIST, FedRAMP, and CIS requirements.
Mentor MLOps, data engineering, and software teams on secure, reliable, and observable ML operations.
Partner with data scientists to move models from experimentation to production, enabling: Reproducible training environments Experiment tracking and model registry workflows Optimized, production-ready inference services
Lead or support incident response and risk analysis related to ML systems (e.g., data quality issues, model drift, security exposures).
Support audits and compliance efforts by documenting ML pipelines, controls, and evidence.
Stay current on MLOps, DevSecOps, and ML platform trends, contributing to PSG’s AI/ML roadmap.