Description

You will own the end-to-end delivery of ML and Generative AI platforms, from architecture design to production deployment.

Responsibilities

• Deploy and scale ML systems in cloud environments, with a focus on Azure.
• Build enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.
• Operationalize agentic AI workflows using LangChain and LangGraph with tool integration.
• Develop reusable infrastructure for GenAI systems using MCP and ADK.
• Establish observability, monitoring, and governance standards for AI pipelines.

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.
• Hands-on experience with RAG systems, embeddings, and vector search.
• Experience with orchestration frameworks: LangChain, LangGraph, and LangSmith.
• Strong knowledge of model serving, inference pipelines, and AI observability.
• Experience with cloud AI ecosystems (Azure AI, Azure ML, Databricks preferred).
• Familiarity with Docker, Kubernetes, and REST APIs for deployment.
• Understanding of CI/CD pipelines and DevOps practices for AI platforms.
• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.

Preferred Skills

• Master’s degree in a relevant technical field.
• Exposure to vector databases such as Pinecone, Weaviate, FAISS, or Azure Cognitive Search.
• Familiarity with enterprise governance frameworks for Responsible AI.

Key Skills

Education

Any Graduate