Experience Requirements:

• 5+ years of experience in DevOps, SRE, or Cloud Engineering

• 2+ years focused on MLOps or ML infrastructure

Other Requirements:

• Subject matter expertise with cloud platforms (AWS, GCP, or Azure)

• Proficiency with ML lifecycle tools such as MLflow, Kubeflow, or SageMaker

• Experience with container orchestration (EKS/GKE/AKS) and model serving frameworks

• Strong scripting and programming skills in Python, Bash, and YAML

• Deep understanding of agentic frameworks

Responsibilities:

• Designing and implementing scalable and secure MLOps pipelines

• Automating the deployment, monitoring, and governance of machine learning models

• Collaborating with Data Scientists and DevOps teams to streamline workflows

• Managing and optimizing ML infrastructure including GPU/TPU provisioning

• Ensuring reproducibility, traceability, and compliance of ML workflows