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Strong global pharma brand and metro location but highly specialized LLM skillset reduces applicant density.
Advanced LLM and agent engineering skills are transferable, but regulated enterprise experience increases domain specificity.
Explicit 9+ years requirement plus mandatory LLM, agent frameworks, async Python, and cloud experience.
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Design, build, and deploy enterprise-grade autonomous multi-agent workflows using orchestration frameworks like LangGraph.
Develop and maintain production-grade AI applications integrating cloud-based LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4) with robust prompt management and conversation state persistence.
Implement observability, evaluation frameworks, security, and compliance to optimize AI solutions supporting productivity and intelligent automation across business functions.
Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or related field.
9+ years of software engineering experience, including 2+ years building and deploying production LLM-powered applications.
Expertise with agentic orchestration frameworks (e.g., LangGraph, LangChain, CrewAI, Autogen) and async Python programming (asyncio).
Experience with FastAPI, cloud platforms (AWS, Azure, Google Cloud), and containerization (Docker); knowledge of enterprise security integration and compliance standards.
Experienced in architecting and scaling autonomous multi-agent AI workflows with complex state management and tool-calling.
Skilled in integrating and optimizing enterprise cloud LLM services with prompt engineering and observability tooling (Langfuse, LangSmith).
Comfortable working in regulated environments with cross-functional global teams, including mentoring and participating in architecture reviews.