Machine Learning Engineer / MLOps Engineer (Mid-Level)

Role Overview

We are seeking a Mid-Level Machine Learning Engineer / MLOps Engineer to serve as a core contributor on a delivery pod of engineers and data scientists. This role focuses on building, deploying, and operating production machine learning systems under the technical guidance of a Distinguished MLOps Engineer Pod Lead.

The ideal candidate has hands-on experience supporting ML pipelines utilizing MLflow and AutoML in production environments and is ready to deepen their expertise in MLOps, cloud platforms, and regulated AI delivery.

Key Responsibilities

Technical Execution

• Contribute to the development, deployment, and operation of ML pipelines supporting production use cases.
• Assist in implementing CI/CD workflows for ML training, testing, and deployment.
• Support model versioning, experiment tracking, monitoring, and retraining workflows using MLflow.
• Help maintain data pipelines, feature engineering processes, and integrations with enterprise data lakes and data warehouses.
• Deploy and operate ML workloads in containerized and Kubernetes-based environments under senior guidance.

Team and Pod Collaboration

• Serve as an active member of a delivery pod with ML engineers, data scientists, and software engineers.
• Participate in code reviews, sprint planning, and technical design discussions.
• Collaborate with senior engineers to troubleshoot production issues and improve system reliability.
• Document ML workflows, deployment procedures, and operational best practices.

Compliance, Security, and Quality

• Follow established security, data privacy, and compliance requirements for commercial and government projects.
• Support bias evaluation, model documentation, and governance practices required in regulated environments.
• Contribute to system reliability, availability, and performance targets for deployed ML services.

Required Qualifications

• U.S. Citizen with eligibility to obtain and maintain a DoD, Intelligence Community, or DHS clearance.
• Bachelors degree in Computer Science, Data Science, Engineering, or a related field, or equivalent professional experience.
• 3 to 5 years of hands-on experience in machine learning engineering, MLOps, or related software engineering roles.
• Strong proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience using MLflow for experiment tracking, model versioning, or lifecycle management.
• Experience deploying or supporting ML workloads in at least one major cloud platform: Azure, AWS, or GCP.
• At least 1 year of experience working with containerization and or Kubernetes-based environments.
• Familiarity with CI/CD concepts and tooling for ML or software systems.
• Solid understanding of software engineering fundamentals including version control, testing, and documentation.

Preferred Qualifications

• Experience supporting ML systems in production environments.
• Exposure to Microsoft Azure services, including Azure ML or Azure DevOps.
• Experience working with data lakes, data warehouses, or large-scale datasets.
• Familiarity with ETL pipelines and basic data modeling concepts.
• Exposure to regulated or compliance-driven environments such as healthcare, biotech, or government.
• Relevant certifications are a plus, including:
  • Microsoft Azure AI Engineer Associate
  • AWS Certified Machine Learning Specialty

Benefits and Growth

• Competitive salary and comprehensive health benefits.
• 401(k) with company matching.
• Clearance sponsorship for eligible candidates.
• Structured mentorship from senior and lead MLOps engineers.
• Access to MLOps, cloud, and AI ethics training.
• Clear growth path into Lead Machine Learning Engineer or MLOps Engineer roles.

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.