Lead MLOps Engineer (Ray, ML Pipelines)

Overview

Remote
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 24 Month(s)

Skills

Amazon Web Services
Cloud Computing
Collaboration
Continuous Delivery
Machine Learning (ML)
Continuous Integration
Distributed Computing
Python
DevOps
Reliability Engineering
Kubernetes
Machine Learning Operations (ML Ops)
Orchestration
Training
Workflow
Agile

Job Details

Lead MLOps Engineer (Ray, ML Pipelines)
Location: Austin, TX (Remote is fine)
Employment Type: C2C Contract

Overview

We are looking for a Lead MLOps Engineer with deep experience in scalable machine learning systems and operationalizing ML pipelines. The ideal candidate will bring strong expertise in the Ray framework for distributed computing and have a solid MLOps background.

Key Responsibilities

  • Design and manage machine learning infrastructure for scalable, distributed training and inference.

  • Implement distributed ML workloads using the Ray framework.

  • Build and deploy end-to-end ML pipelines in a production environment.

  • Integrate with cloud-native services and automate deployment workflows.

  • Monitor and optimize model performance and system reliability.

  • Collaborate closely with Data Scientists, ML Engineers, and DevOps teams.

Required Skills

  • 7+ years of experience in MLOps or Machine Learning Engineering.

  • Hands-on experience with Ray ().

  • Strong programming skills in Python.

  • Experience with ML orchestration tools like Kubeflow, Airflow, or MLflow.

  • Familiarity with Kubernetes and containerized ML deployments.

  • Strong knowledge of cloud platforms (preferably AWS).

  • Experience working in Agile and CI/CD environments.

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