Description

Deploy, scale, and operate ML and Generative AI systems in cloud-based production environments, with a preference for Azure.

This role is hybrid.

Responsibilities

• Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.
• Implement agentic AI workflows with tool use, leveraging frameworks such as LangChain and LangGraph.
• Design model and agent serving architectures, including APIs, batch inference, and real-time workflows.
• Operationalize MLOps and LLM Ops practices, including CI/CD automation, versioning, testing, and lifecycle management.
• Establish best practices for observability, monitoring, evaluation, and governance of GenAI pipelines.

Required Skills

• 8–10 years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment.
• Strong proficiency in Python for production-ready AI/ML services.
• Experience with LangChain, LangGraph, and LangSmith.
• Familiarity with Azure AI, Azure ML, and Databricks.
• Experience with Docker, Kubernetes, and REST APIs.
• Knowledge of model serving, monitoring, and observability for AI systems.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.

Preferred Skills

• Experience with Model Context Protocol (MCP) and AI Development Kit (ADK).
• Proven track record of deploying GenAI in enterprise environments.

Key Skills

Education

Any Graduate