Description

Deploy and scale Machine Learning and Generative AI systems in cloud-based production environments.

This role is on-site.

Responsibilities

• Build enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.
• Implement agentic AI workflows with tool use, leveraging Lang Chain and Lang Graph.
• Develop reusable infrastructure and orchestration for GenAI systems using Model Context Protocol (MCP) and AI Development Kit (ADK).
• 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.

Required Skills

• 8–10 years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment.
• Strong proficiency in Python for building production-ready AI/ML services.
• Experience deploying GenAI applications in enterprise environments.
• Proficiency with orchestration frameworks: Lang Chain, Lang Graph, and Lang Smith.
• Strong knowledge of model serving, inference pipelines, and AI observability.
• Experience with cloud AI ecosystems (Azure AI, Azure ML, Databricks).
• Familiarity with Docker and Kubernetes for containerization and deployment.

Preferred Skills

• Experience with CI/CD automation and MLOps/LLM Ops lifecycle management.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.

Key Skills

Education

Any Graduate