Lead I - ML Engineering (ML Ops , Agentic AI and AWS)
UST (company site, opens in a new tab)7+ yrsHyderabadOn-siteDirect career pageEst. 20–40 LPA
Posted on 2 Sept
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Job description
Structured overview of role & requirements
About This Role
- Build, deploy, and manage end-to-end machine learning lifecycle pipelines including automation of training, testing, deployment, and monitoring.
- Implement and maintain model versioning, experiment tracking, and model registry to ensure operational governance and auditability.
- Collaborate across Data Engineering and DevOps teams to operationalize ML solutions with a focus on performance monitoring and security controls.
Minimum Requirements
- 4+ years relevant experience as MLOps Engineer, 7+ years total industrial experience.
- Strong Python programming skills with hands-on experience in MLflow, Kubeflow, Amazon SageMaker, AWS Step functions, and Amazon 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 Proof of Concept to Production in enterprise cloud environments, preferably AWS.
- Skilled at integrating ML lifecycle management tools and automation platforms to optimize production model monitoring and governance.
- Familiar with advanced topics such as Responsible AI, AI governance, generative AI, LLMOps, and data engineering tools (e.g., Databricks, Spark).