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Senior MLOps Engineer

Job in Goodyear, Maricopa County, Arizona, 85338, USA
Listing for: CHEQUESPREAD PLC
Full Time position
Listed on 2025-12-20
Job specializations:
  • IT/Tech
    AI Engineer, Data Engineer, Machine Learning/ ML Engineer, Cloud Computing
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below

Join to apply for the Senior MLOps Engineer role at CHEQUE SPREAD PLC
.

Description

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, Dev Sec Ops  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.

Key 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.
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
    • Dev Ops / Dev Sec Ops  / SRE for ML workloads
    • Data engineering with production ML workflows
    • Applied ML in production environments
  • Strong experience with secure CI/CD pipelines and IaC (Git Lab CI, Jenkins, Git Hub 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 (Num Py, pandas, scikit‑learn, PyTorch, Tensor Flow).
  • Strong scripting skills (Python, Bash, Power Shell) 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.
Preferred Qualifications
  • Master’s degree in a related field.
  • Active Security Clearance above minimum requirements (SCI, CI Poly).
  • Industry…
Position Requirements
10+ Years work experience
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