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BMS brand and Hyderabad metro increase competition, but senior niche LLM/agent specialization limits the applicant pool.
Specialized LLM/agent engineering skills are transferable across industries, but life-sciences domain experience is a meaningful plus.
Explicit 9+ years requirement plus mandatory LLM production experience and strict tech stack increases filter rigidity.
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Lead design, development, and deployment of complex autonomous multi-agent workflows using orchestration frameworks (e.g., LangGraph, CrewAI).
Architect and maintain production-grade async Python applications integrating cloud-based LLMs, APIs, and observability tools for AI agent orchestration.
Mentor engineering teams on async programming, agent design, and LLMOps best practices while ensuring compliance with security and data governance standards.
Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or related discipline.
9+ years in software engineering with at least 2 years building production LLM-powered applications.
Proven expertise with agent orchestration frameworks (preferably LangGraph), async Python (FastAPI), cloud LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4).
Experience with Docker, Git, CI/CD, cloud platforms (AWS/Azure/GCP), and production async web frameworks.
Experienced leader capable of managing complex multi-agent AI workflows beyond simple chatbot architectures involving conditional logic and tool integration.
Strong domain knowledge in async Python and LLMOps with familiarity in agent design patterns like ReAct and Plan-and-Execute.
Comfortable working with globally distributed teams and able to collaborate with data engineers, analysts, and UX teams in regulated environments.