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Senior, niche LLM/agent skills and known pharma brand create moderate applicant competition.
Specialized LLM and agent-engineering skills are transferable across industries but retain moderate domain specificity.
Explicit 9+ years, 2+ years LLM production experience and specific tech stack make shortlisting highly strict.
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Design, build, and deploy complex autonomous multi-agent AI workflows with orchestration frameworks (LangGraph preferred) involving 10+ nodes, conditional routing, and parallel execution.
Develop and maintain reusable agent node libraries, testing frameworks, and production-grade FastAPI applications integrating cloud LLMs (AWS Bedrock, Azure OpenAI, etc.) and enterprise services.
Lead implementation of observability, security, evaluation frameworks for AI agent workflows; mentor engineers; partner cross-functionally to translate business needs into scalable AI solutions.
Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or related field.
9+ years of software engineering experience; at least 2 years building production LLM-powered applications.
Proven expertise with agent orchestration frameworks (LangGraph preferred), cloud LLM providers (AWS Bedrock, Azure OpenAI, Anthropic, OpenAI GPT-4), async Python programming, FastAPI, containerization, and cloud platforms (AWS/Azure/GCP).
Experience designing autonomous agent systems with multi-step tasks, state management, tool-calling protocols, and observability tooling for LLMOps.
Senior engineering leader with deep hands-on expertise in building, deploying, and maintaining enterprise-scale AI agent orchestration systems.
Demonstrated ability to architect complex multi-agent workflows involving asynchronous patterns, real-time streaming interfaces, and hybrid intelligence paradigms.
Experience working with globally distributed teams, managing offshore developers, and collaborating with cross-functional stakeholders in regulated or life sciences environments.