Lead MLOps

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Requirements:

• Hands-on experience with Kubernetes, Docker, and containerised ML workloads.
• Strong expertise in AWS, Azure, or Google Cloud, with knowledge of GPU-based computing.
• Experience in CI/CD pipelines for machine learning (e.g., GitHub Actions, MLflow, Kubeflow).
• Proficiency in Python (experience with Go or Java is a plus).
• Strong experience in scripting & automation (Bash, Terraform, Ansible).
• Knowledge of vector & graph databases (e.g., Neo4j, Milvus, Pinecone).
• Experience managing distributed data storage & processing.

Responsibilities:

• Deploy and manage ML models in production environments, ensuring scalability and reliability.
• Design and maintain ML pipelines, automating training, validation, and deployment workflows.
• Optimize AI/ML infrastructure for performance, cost, and efficiency across cloud and on-premise systems.
• Integrate and manage vector and graph databases (e.g., Neo4j, Pinecone, Milvus) for AI-driven applications.
• Implement observability & monitoring solutions for model performance, data drift, and system health.
• Ensure compliance with security and data governance best practices in ML deployment.
• Collaborate with Data Scientists and DevOps teams to streamline AI model lifecycle management.