Machine learning MLOps Lead Engineer

MLOps Lead Engineer
TX/Dallas- May require travel to plants

Custom Skill Requirements

MLOps
CI/CD pipelines
AWS
Python
TensorFlow
Terraform

Contractor Qualifying Questions
Are you local to Dallas?
Are you able to go to Dallas office 3days/wk?
Are you able to travel to a manufacturing plant?
Do you have MLOps Engineering experience?
Can you build MLOps pipeline?
Have you worked on Edge deployment?

We are seeking an MLOps Engineer to bridge the gap between data science and production systems, ensuring that machine learning models are deployed, monitored, and maintained at scale. You will work closely with data scientists, data engineers, and software developers to design and implement automated, reliable, and secure ML pipelines from development to production.

Key Responsibilities

Model Deployment & Serving

Deploy ML models into production environments using tools such as Docker, Kubernetes, and model serving frameworks (e.g., TensorFlow Serving, TorchServe, MLflow).

Implement CI/CD pipelines for ML workflows.

Pipeline Development & Automation

Build and maintain end-to-end machine learning pipelines for data ingestion, preprocessing, training, validation, deployment, and monitoring.

Automate model retraining and versioning to ensure continuous improvement.

Monitoring & Maintenance

Set up monitoring and alerting systems for model performance, data drift, and infrastructure health.

Troubleshoot and resolve model degradation issues in production.

Collaboration & Integration

Collaborate with data scientists to transition models from experimentation to production-ready systems.

Work with DevOps and cloud teams to ensure ML workloads are scalable and cost-efficient.

Security & Compliance

Ensure compliance with data governance, security, and privacy regulations.

Manage role-based access control (RBAC) for ML infrastructure.
MLOps pipeline automation