Role Overview

We are seeking a Staff Machine Learning Engineer / MLOps Pod Lead to lead a delivery pod of 3 to 5 engineers and data scientists responsible for designing, deploying, and operating scalable machine learning pipelines integrated with enterprise data platforms.

This is a hands-on, delivery-focused role that combines deep MLOps execution with technical leadership in a collaborative, in-office environment. The role supports both active commercial engagements and federal and state government programs, adapting ML systems to varying regulatory, security, and operational requirements.

Key Responsibilities

Technical Architecture and MLOps Execution

• Design, deploy, and operate end-to-end ML pipelines supporting TB-scale datasets integrated with enterprise data lakes and data warehouses.

• Build and maintain production-grade MLOps systems across Azure, AWS, and GCP, with primary emphasis on Microsoft Azure.

• Implement CI/CD pipelines for machine learning workflows, including model training, versioning, deployment, and monitoring.

• Utilize MLflow for experiment tracking, model registry, and lifecycle management.

• Monitor model performance, data drift, and system reliability in production environments.

• Ensure ML services and pipelines meet defined SLA targets across regulated and semi-regulated environments.

• Partner with Data Engineering teams on ETL workflows, feature pipelines, and data modeling strategies.

• Deploy and operate ML workloads on Kubernetes-based platforms.

Leadership and Delivery

• Lead a pod of 3 to 5 engineers and data scientists, providing technical direction, mentorship, and code reviews.

• Own delivery outcomes across mixed commercial and public sector portfolios, balancing delivery velocity with compliance and quality requirements.

• Translate business, mission, and regulatory requirements into scalable, maintainable ML system designs.

Compliance, Security, and Ethical AI

• Implement ML systems aligned with applicable standards, including the NIST AI Risk Management Framework for public sector programs.

• Support secure handling of sensitive bioscience, health, and government data.

• Apply bias detection, mitigation, and documentation practices across deployed models.

Required Qualifications

• 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 or more years of hands-on experience in machine learning engineering and MLOps supporting production deployments.

• Strong proficiency in Python and experience with modern ML frameworks such as MLflow, PyTorch, and

  TensorFlow.

• Hands-on experience deploying ML workloads in cloud environments including Azure, AWS, or GCP, with demonstrated depth in Microsoft Azure.

• 1 to 2 years of hands-on experience deploying or operating Kubernetes workloads in production environments.

• Experience with Azure ML, Azure DevOps, or Azure-native data and ML services strongly preferred.

• Hands-on experience with CI/CD for ML systems, data lakes and warehouses, and large-scale ETL and data modeling.

• Solid grounding in software engineering best practices, including testing, version control, and documentation.

Preferred Qualifications

• Experience delivering ML systems for commercial clients and or federal or state government programs.

• Prior technical leadership experience guiding small engineering teams.

• Experience operating ML systems in Azure Government or other regulated cloud environments.

• Familiarity with Terraform or other infrastructure-as-code tools.

• Exposure to AI governance, model risk management, or ethical AI frameworks.

• Relevant certifications are a plus, including:

  • Microsoft Azure AI Engineer Associate

  • Microsoft Azure Data Scientist Associate

  • AWS Certified Machine Learning Specialty

  • TensorFlow Developer Certificate

Benefits and Growth

• Competitive salary and comprehensive health benefits.

• 401(k) with company matching.

• Clearance sponsorship for eligible candidates.

• Access to MLOps, AI ethics, and public sector compliance training.

• Clear growth path into Lead, Principal, or Staff-level roles as programs expand across commercial and government portfolios.

Equal Employment Opportunity

General Genomics, Inc. is an Equal Employment Opportunity employer committed to building a diverse and inclusive workforce. We provide equal opportunity to all applicants without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, or veteran status, in full compliance with applicable federal, Oklahoma, and Texas employment laws.