Machine Learning Engineer / MLOps Engineer (Mid-Level)

PTF Consulting • Fort Worth, TX

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About The Position

Role Overview We are seeking a Mid-Level Machine Learning Engineer / MLOps Engineer to join a delivery pod of engineers and data scientists building production ML systems. This role blends hands-on machine learning development with operational MLOps responsibilities and works under the technical direction of a Senior or Lead MLOps Pod Lead. This position is ideal for an engineer who has supported ML systems beyond experimentation and is ready to deepen their experience in production ML, cloud platforms, and regulated AI delivery. Responsibilities Machine Learning & MLOps Execution Contribute to the development, deployment, and operation of production machine learning pipelines. Support model training, evaluation, packaging, and deployment workflows. Assist in building and maintaining CI/CD pipelines for ML training, testing, and inference. Support model versioning, monitoring, retraining, and performance tracking. Help maintain data pipelines, feature engineering processes, and integrations with data lakes and warehouses. Deploy and operate ML workloads in containerized and Kubernetes-based environments with senior guidance. Team & Pod Collaboration Actively participate in a cross-functional delivery pod alongside ML engineers, data scientists, and software engineers. Contribute to code reviews, sprint planning, and technical design discussions. Collaborate with senior engineers to troubleshoot production issues and improve system reliability. Produce and maintain documentation for ML workflows, deployments, and operational procedures. Security, Compliance & Quality Adhere to established security, data privacy, and compliance requirements across commercial and government projects. Support model documentation, explainability, and bias evaluation practices required in regulated environments. Contribute to system reliability, performance, and availability targets for deployed ML services.

Requirements

• U.S. Citizen with the ability 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.
• 3–5 years of experience in machine learning engineering, MLOps, or related software engineering roles.
• Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch or TensorFlow.
• Experience deploying or supporting ML workloads in at least one major cloud platform (Azure, AWS, or GCP).
• At least 1 year of experience with containerization and/or Kubernetes in development or production 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.

Nice To Haves

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

Responsibilities

• Contribute to the development, deployment, and operation of production machine learning pipelines.
• Support model training, evaluation, packaging, and deployment workflows.
• Assist in building and maintaining CI/CD pipelines for ML training, testing, and inference.
• Support model versioning, monitoring, retraining, and performance tracking.
• Help maintain data pipelines, feature engineering processes, and integrations with data lakes and warehouses.
• Deploy and operate ML workloads in containerized and Kubernetes-based environments with senior guidance.
• Actively participate in a cross-functional delivery pod alongside ML engineers, data scientists, and software engineers.
• Contribute to code reviews, sprint planning, and technical design discussions.
• Collaborate with senior engineers to troubleshoot production issues and improve system reliability.
• Produce and maintain documentation for ML workflows, deployments, and operational procedures.
• Adhere to established security, data privacy, and compliance requirements across commercial and government projects.
• Support model documentation, explainability, and bias evaluation practices required in regulated environments.
• Contribute to system reliability, performance, and availability targets for deployed ML services.

Benefits

• 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 training in MLOps, cloud platforms, and ethical AI delivery.
• Defined career progression into Senior Machine Learning Engineer or Senior MLOps Engineer roles.

What This Job Offers

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