Senior MLOps Lead - Build Production ML; Fort Worth

Invictus Strategy & Solutions is a Service-Disabled Veteran-Owned Small Business (SDVOSB) providing strategic workforce solutions to mission-critical government and commercial operations. From cleared federal programs to complex industrial projects, we deliver top-tier professionals who drive performance, safety, and results. Our commitment to operational excellence and customer alignment makes us the partner of choice for organizations seeking agile and reliable talent solutions.

Summary

Invictus Strategy & Solutions is seeking a Senior Machine Learning Engineer and MLOps POD Lead to join our growing technical delivery team in Fort Worth, Texas. This on-site role requires strong hands-on experience designing, deploying, and operating production grade machine learning systems integrated with enterprise data platforms.

The selected candidate will lead a small delivery pod of three to five engineers and data scientists responsible for building and operating scalable machine learning pipelines. This role combines hands on MLOps execution with technical leadership, ensuring machine learning systems operate reliably across commercial and government environments with varying regulatory, security, and operational requirements.

This role requires an engineer who remains directly involved in architecture, pipeline design, and production operations while guiding a small team responsible for delivery outcomes.

Key Responsibilities

MLOps Architecture and Execution

• Design, deploy, and operate end to end machine learning pipelines supporting large scale datasets integrated with enterprise data lakes and data warehouses
• Build and maintain production grade MLOps systems across cloud platforms including Azure, AWS, and GCP with primary emphasis on Microsoft Azure
• Implement CI and CD pipelines supporting model training, versioning, deployment, and lifecycle management
• Utilize MLflow for experiment tracking, model registry management, and model lifecycle governance
• Monitor model performance, data drift, and system reliability across production environments
• Ensure machine learning services meet defined reliability and SLA expectations
• Collaborate with Data Engineering teams to integrate ML pipelines with ETL workflows, feature engineering pipelines, and enterprise data platforms
• Deploy and manage ML workloads on Kubernetes based environments

Technical Leadership and Delivery

• Lead a delivery pod of three to five engineers and data scientists responsible for building and operating ML systems
• Provide technical guidance, mentorship, and code review support to team members
• Translate business, operational, and regulatory requirements into scalable ML system architectures
• Own delivery outcomes across commercial and public sector engagements while maintaining quality, security, and compliance requirements

Security, Compliance, and Responsible AI

• Support machine learning implementations aligned with applicable standards including the NIST AI Risk Management Framework
• Ensure secure handling of sensitive data including healthcare, bioscience, or government datasets
• Support governance and operational oversight for machine learning lifecycle management

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related discipline, or equivalent professional experience
• U.S. Citizenship with the ability to obtain and maintain a government security clearance
• Minimum seven years of experience in machine learning engineering or MLOps supporting production systems
• Strong proficiency in Python and experience with modern ML frameworks such as PyTorch, Tensor Flow, or similar tools
• Hands on experience designing and deploying machine learning systems in cloud environments including Azure, AWS, or GCP, with demonstrated depth in Microsoft Azure environments
• Experience implementing CI and CD pipelines for machine learning workflows
• Hands on experience supporting enterprise data platforms including data lakes, data warehouses, and ETL pipelines
• Experience deploying or operating workloads on Kubernetes based platforms
• Strong foundation in software engineering best practices including version control, automated testing, and documentation

Prefer…