ML Ops Lead

We have Contract role ML Ops Lead for our client at Dallas TX. Please let me know if you or any of your friends would be interested in this position.

Position Details:

ML Ops Lead-Dallas TX

Location : Dallas, TX 75201 Project Duration : 6+ Month Contract to hire

Pay rate : $65/hr. on W2

Job Summary:

• Build & Automate ML Pipelines: Design, implement, and maintain CI/CD pipelines for machine learning models, ensuring automated data ingestion, model training, testing, versioning, and deployment.
• Operationalize Models: Collaborate closely with data scientists to containerize, optimize, and deploy their models to production, focusing on reproducibility, scalability, and performance.
• Infrastructure Management: Design and manage the underlying cloud infrastructure (AWS) that powers our ML Ops platform, leveraging Infrastructure-as-Code (IaC) tools to ensure consistency and cost optimization.
• Monitoring & Observability: Implement comprehensive monitoring, alerting, and logging solutions to track model performance, data integrity, and pipeline health in real-time. Proactively address issues like model or data drift.
• Governance & Security: Establish and enforce best practices for model and data versioning, auditability, security, and access control across the entire machine learning lifecycle.
• Tooling & Frameworks: Develop and maintain reusable tools and frameworks to accelerate the ML development process and empower data science teams.

Job Description:

• Cloud Expertise: Extensive hands-on experience in designing and implementing ML Ops solutions on AWS. Proficient with core services like Sage Maker, S3, ECS, EKS, Lambda, SQS, SNS, and IAM.
• Coding & Automation: Strong coding proficiency in Python. Extensive experience with automation tools, including Terraform for IaC and GitHub Actions.
• ML Ops & DevOps: A solid understanding of ML Ops and DevOps principles. Hands-on experience with ML Ops frameworks like Sage maker Pipelines, Model Registry, Weights and Bias, ML flow or Kube flow and orchestration tools like Airflow or Argo Workflows.
• Containerization: Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
• Model Lifecycle: Experience with model testing, validation, and performance monitoring. Good understanding of ML frameworks like Py Torch or Tensor Flow is required to effectively collaborate with data scientists.
• Communication: Excellent communication and documentation skills, with a proven ability to collaborate with cross-functional teams (data scientists, data engineers, and architects).