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Metro Tier-1 employer plus senior specialized LLM role yields moderate competition.
LLM and agent engineering skills are broadly transferable, though life-sciences compliance adds moderate domain specificity.
Explicit 9+ years and many mandatory LLM, async Python, cloud, and compliance requirements make hiring filters strict.
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Design, build, and deploy autonomous multi-agent workflows with 10+ nodes involving complex orchestration, task decomposition, and error recovery using frameworks like LangGraph, CrewAI, or Autogen.
Develop and maintain reusable agent libraries, testing frameworks, prompt management systems, and integrate cloud LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4) into production FastAPI applications with real-time streaming and state persistence.
Implement observability and evaluation frameworks to monitor agent performance, optimize costs, ensure enterprise security and compliance, and collaborate cross-functionally to translate business requirements into scalable agent workflows.
Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or related discipline.
9+ years of software engineering experience, with 2+ years building and deploying production LLM-powered applications.
Proven experience with agentic orchestration frameworks (LangGraph preferred), cloud LLM platforms (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4), FastAPI with async Python, cloud platforms (AWS, Azure, GCP), Docker, Git, and CI/CD pipelines.
Expert-level async Python programming and demonstrated experience building autonomous multi-step agent systems with conditional logic and tool-calling patterns.
Senior software engineer with deep expertise in autonomous agent design and multi-agent orchestration frameworks, particularly LangGraph or similar.
Experience operating in cloud native production environments integrating multiple LLM providers and observability tooling, with strong focus on scalability, reliability, and cost optimization.
Able to work effectively in global teams with cross-functional stakeholders and mentor junior engineers on advanced async Python patterns, LLMOps, and agent workflow best practices.