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Job Description
Structured overview of role & requirementsAbout This Role
Build, deploy, and manage end-to-end machine learning lifecycle pipelines including automation of training, testing, deployment, and monitoring.
Implement model versioning, experiment tracking, and model registry while monitoring model performance and drift in production.
Collaborate with Data Engineers and DevOps teams to operationalize ML solutions and establish governance, security, access control, and audit processes for ML platforms.
Minimum Requirements
4+ years industrial experience relevant to ML Ops engineering; total work experience 7+ years.
Strong Python programming skills and hands-on expertise with MLflow, Kubeflow, Amazon SageMaker, AWS Step functions, and ECS.
Experience with Docker, Kubernetes, CI/CD tools, DevSecOps practices, and Infrastructure as Code tools like Terraform or CloudFormation.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
Ideal Candidate Profile
Experienced in taking ML/AI solutions from PoC to production with strong operational ownership of ML lifecycle.
Skilled in end-to-end MLOps platform implementation including governance, security, and monitoring in cloud environments, especially AWS.
Able to coordinate across teams including Data Engineering and DevOps, and who understands advanced topics like Responsible AI, AI governance, and emerging trends like Generative AI and LLMOps.