Description

Deploy, scale, and operate production-grade ML and Generative AI systems in cloud environments, focusing on RAG pipelines and agentic AI workflows.

Responsibilities

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

Required Skills

• 7+ years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment.
• Strong proficiency in Python for building production-ready AI/ML services.
• Hands-on experience with orchestration frameworks: LangChain, LangGraph, and LangSmith.
• Experience deploying Generative AI applications in real-world enterprise environments.
• Knowledge of model serving, inference pipelines, monitoring, and observability for AI systems.
• Proficiency with containerization and deployment tools: Docker, Kubernetes, and REST APIs.
• Experience working with cloud AI ecosystems, specifically Azure AI and Azure ML.

Preferred Skills

• Experience with Azure Databricks, Data Lake, Synapse, or related Azure services.
• Familiarity with vector databases such as Pinecone, Weaviate, FAISS, or Azure Cognitive Search.

Key Skills

Education

Any Graduate