Description

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

Responsibilities

• Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.
• Implement and operationalise agentic AI workflows using tool use and frameworks like Lang Chain and Lang Graph.
• Develop reusable infrastructure and orchestration for GenAI systems using MCP and 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-ready AI/ML services.
• Proven experience deploying and supporting GenAI applications in enterprise environments.
• Experience with orchestration frameworks, including Lang Chain, Lang Graph, and Lang Smith.
• Strong knowledge of model serving inference pipelines, monitoring, and observability for AI systems.
• Experience with cloud AI ecosystems, specifically Azure AI or Azure ML.
• Familiarity with containerization and deployment tools such as Docker and Kubernetes.
• Experience building RESTful services.

Key Skills

Education

Any Graduate