MLOps Lead Engineer

Overview

Remote
Hybrid
Depends on Experience
Contract - W2
Contract - 12 Month(s)
No Travel Required

Skills

Machine Learning (ML)
Machine Learning Operations (ML Ops)
Kubernetes
Management
Manufacturing
Continuous Integration
Data Governance
Data Science
DevOps
Docker
Privacy
RBAC
Regulatory Compliance
TensorFlow
Continuous Improvement
Cloud Computing
Collaboration
Continuous Delivery
Access Control
Workflow
Edge deployment
Terraform
AWS
Python
MLOps
CI/CD pipelines

Job Details

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

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