Description

Key Responsibilities:

• Design and develop GenAI applications using Python and modern AI frameworks.
• Build agentic AI workflows capable of planning, reasoning, tool calling, and executing multi-step processes.
• Develop advanced RAG solutions, vector search, document chunking, hybrid retrieval, and knowledge integration.
• Work with vector databases and knowledge graphs such as Neo4j.
• Deploy scalable AI applications using Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
• Implement CI/CD, production monitoring, evaluation, tracing, and observability for AI applications.
• Optimize LLM applications for latency, throughput, reliability, and cost.
• Implement AI safety, security, data privacy, testing, and governance controls.
• Provide technical leadership, mentor engineers, and drive architecture and engineering best practices.

Required Skills:

• Expert-level Python
• LangChain, LlamaIndex, LangGraph, CrewAI, or AutoGen
• RAG, vector databases, hybrid search, and document retrieval
• AI agents and tool-calling workflows
• Docker and Kubernetes
• AWS, Azure, or GCP
• CI/CD and MLOps
• AI observability, evaluation, and tracing

Key Skills

Education

Bachelor's degree