Responsibilities

  • Design and develop AI-powered applications using Python and modern LLM frameworks, ensuring production readiness and architectural alignment.
  • Build and orchestrate LLM workflows using LangChain and LangGraph with a focus on agent orchestration and workflow management.
  • Develop AI agents and multi-agent systems that interact with enterprise tools, APIs, and data platforms for intelligent task execution.
  • Integrate LLM models such as OpenAI GPT, Claude, Llama, or open-source alternatives with a focus on reliability and maintainability.
  • Implement Model Context Protocol to enable structured interaction between LLMs and enterprise systems with consistent context handling.
  • Design prompt engineering strategies and structured output pipelines to improve response quality and control model behavior.
  • Build and optimize RAG pipelines using vector databases to support context-aware retrieval and knowledge-driven applications.
  • Integrate AI solutions with REST APIs and microservices, ensuring seamless data exchange and workflow orchestration across systems.
  • Implement monitoring, evaluation, and guardrails to ensure responsible AI usage.
  • Optimize inference performance and cost through efficient architecture and workload management.