Description

Key Responsibilities

Design and build production grade multi agent systems using LangGraph with experience in LangChain CrewAI and AutoGen Architect agent orchestration patterns including planning tool usage persistent state management memory reflection and multi agent coordination Develop and optimize Retrieval Augmented Generation RAG pipelines including document processing chunking embedding workflows and vector database integration Build agent evaluation testing and observability frameworks for production AI systems Develop natural language to data query solutions integrating with Databricks Genie or similar platforms Integrate LLM and SLM services including OpenAI Azure OpenAI Anthropic and open source models Design prompt engineering strategies including chain of thought few shot prompting structured outputs and function calling Implement AI guardrails safety mechanisms and content filtering Evaluate AI models based on latency accuracy cost and business performance Develop scalable Python backend services using FastAPI Implement caching rate limiting persistent state conversation memory and distributed task processing using Celery Develop event driven microservices using Dapr and real time streaming with SSE Build APIs integrating AI agents with enterprise systems databases and external services Implement autoscaling using KEDA and containerized deployments Lead AI proof of concepts and transition successful solutions into production Mentor engineering teams on AI best practices prompt engineering and agent design

Key Skills

Education

Bachelor’s degree