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Strong employer brand and metro location but specialized LLM/agent skillset limits applicant density.
ML/LLM engineering skills are broadly transferable across industries, so background sensitivity is low.
Explicit 9+ years, 2+ years LLM production experience, and many mandatory tech requirements drive strict screening.
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Design, build, and deploy complex autonomous multi-agent workflows with orchestration frameworks like LangGraph, including error recovery and parallel execution.
Develop and maintain reusable libraries, testing frameworks, and production-grade FastAPI async applications integrating cloud LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4) and enterprise services.
Lead observability, evaluation, security integration, and collaborate with cross-functional teams; mentor junior engineers and contribute to architecture and knowledge sharing.
Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or related discipline.
9+ years of software engineering experience, including 2+ years building and deploying production LLM-powered applications.
Proven experience with agent orchestration frameworks (LangGraph preferred), cloud LLM providers, async Python (especially asyncio), FastAPI, Docker, Git, CI/CD, and cloud platforms (AWS, Azure, GCP).
Not explicitly mentioned in the JD: notice period, mandatory location, or strict regulatory constraints.
Experienced in architecting and operating advanced autonomous agent systems involving multi-step, conditional logic with tool-calling patterns.
Comfortable working in globally distributed teams, collaborating with data engineers, business analysts, UX teams, and mentoring junior engineers.
Familiar with LLMOps best practices including prompt engineering, observability tooling, conversation state management, and enterprise security compliance.