Sr. Machine Learning Engineer & MLOps POD Lead

PTF Consulting • Fort Worth, TX

2d • Onsite

About The Position

Role Overview We are seeking a Senior Machine Learning Engineer & MLOps Pod Lead to lead a delivery pod of 3–5 engineers and data scientists responsible for designing, deploying, and operating production-grade ML systems integrated with enterprise data platforms. This is a hands-on technical leadership role , blending deep MLOps execution with delivery ownership. The Pod Lead is accountable for technical design decisions, delivery quality, system reliability, and mentorship within an in-office, collaborative environment. This role supports active commercial engagements as well as federal and state government programs, adapting ML solutions to diverse regulatory, security, and operational requirements.

Requirements

• U.S. Citizen with an active DoD, Intelligence Community, or DHS clearance, or eligibility to obtain and maintain one.
• Bachelors degree in Computer Science, Data Science, Engineering, or a related field, or equivalent professional experience.
• 7+ years of hands-on experience in machine learning engineering and/or MLOps with production deployments.
• Strong proficiency in Python and experience with modern ML frameworks such as PyTorch or TensorFlow.
• Demonstrated experience deploying ML workloads in cloud environments (Azure, AWS, or GCP), with depth in Microsoft Azure.
• 1–2 years of hands-on experience deploying and operating workloads on Kubernetes in production.
• Hands-on experience with CI/CD for ML systems, data lakes and warehouses, and large-scale ETL and data modeling.
• Strong grounding in software engineering best practices, including testing, version control, documentation, and code quality.

Nice To Haves

• Experience delivering ML systems for commercial clients and federal or state government programs.
• Prior technical leadership experience guiding small engineering teams or delivery pods.
• Experience operating ML systems in Azure Government or other regulated cloud environments.
• Familiarity with infrastructure-as-code tools such as Terraform.
• Exposure to AI governance, model risk management, or ethical AI frameworks.
• Relevant certifications, including: Microsoft Azure AI Engineer Associate Microsoft Azure Data Scientist Associate AWS Certified Machine Learning – Specialty TensorFlow Developer Certificate

Responsibilities

• Technical Architecture & Execution Design, deploy, and operate end-to-end machine learning pipelines supporting TB-scale datasets integrated with enterprise data lakes and warehouses.
• Architect and maintain production-grade MLOps platforms across Azure, AWS, and/or GCP, with primary deployment emphasis on Microsoft Azure.
• Build and own CI/CD pipelines for ML training, testing, versioning, deployment, and monitoring.
• Establish monitoring for model performance, data drift, system health, and operational reliability.
• Ensure ML services meet availability, performance, and scalability targets, including 99.9% uptime requirements.
• Partner with data engineering teams on ETL workflows, feature pipelines, and data modeling strategies.
• Deploy and operate ML workloads on Kubernetes-based platforms in production environments.
• Pod Leadership & Delivery Ownership Lead a pod of 3–5 engineers and data scientists, providing technical direction, mentorship, and code reviews.
• Own delivery outcomes across a mixed portfolio of commercial and public sector projects.
• Balance delivery velocity with security, compliance, and operational risk considerations.
• Translate business, mission, and regulatory requirements into scalable, maintainable ML system designs.
• Serve as the technical escalation point for production issues and architectural decisions.
• Security, Compliance & Ethical AI Design and operate ML systems aligned with applicable public sector standards, including the NIST AI Risk Management Framework.
• Ensure secure handling of sensitive bioscience, healthcare, and government data.
• Implement bias detection, mitigation, explainability, and documentation practices across deployed models.
• Support audit readiness and compliance documentation for regulated environments.

Benefits

• Competitive salary and comprehensive health benefits.
• 401(k) with company matching.
• Clearance sponsorship for eligible candidates.
• Access to advanced training in MLOps, ethical AI, and public sector compliance.
• Clear growth path into Lead, Principal, or Staff-level engineering roles as programs expand.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level