Description

You will design, deploy, and operate ML and Generative AI systems in cloud production environments.

This role is on-site.

Responsibilities

• Build and manage enterprise-grade Retrieval Augmented Generation (RAG) applications using embeddings and vector search.
• Implement and operationalize agentic AI workflows with tool use using frameworks like LangChain and LangGraph.
• Develop reusable infrastructure and orchestration for GenAI systems using Model Context Protocol (MCP) and AI Development Kit (ADK).
• Design and implement model and agent serving architectures, covering APIs, batch inference, and real-time workflows.
• Drive adoption of MLOps/LLMOps practices, including CI/CD automation, versioning, and lifecycle management.

Required Skills

• 8+ years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment.
• Strong proficiency in Python for building production-grade AI/ML services.
• Proven experience deploying and supporting GenAI applications in enterprise environments.
• Hands-on experience with RAG systems, embeddings, vector search, and retrieval pipelines.
• Experience with orchestration frameworks including LangChain, LangGraph, and LangSmith.
• Strong knowledge of model serving, inference pipelines, monitoring, and observability for AI systems.
• Experience working with cloud AI ecosystems (Azure AI, Azure ML, Databricks preferred).
• Familiarity with containerization and deployment tools (Docker, Kubernetes, REST APIs).

Key Skills

Education

Any Graduate