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Tier-1 employer, mid-level popular AI role, metro location, and broad skillset create high competition.
Role demands specialized LLM production and backend expertise, limiting cross-industry interchangeability.
Explicit 2–4 year requirement plus mandatory production LLM and backend experience increase hiring rigidity.
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Develop and maintain scalable backend AI systems, including APIs integrating LLMs like OpenAI, Anthropic, Gemini, and Azure OpenAI.
Build and manage complex Retrieval-Augmented Generation (RAG) pipelines, knowledge bases, and agentic AI workflows using frameworks such as LangGraph and AutoGen.
Own AI system operational aspects like prompt/context management, latency optimization, embedding strategies, cost monitoring, and observability including tool integration and evaluation pipelines.
2 - 4 years of relevant experience.
Strong backend development skills in Python, Node.js, Java, or Go.
Experience integrating LLM APIs in production with knowledge of RAG, embeddings, and vector databases.
Experience with API design (REST/GraphQL), SQL/NoSQL, asynchronous systems, and cloud deployment (AWS/GCP/Azure).
4–7 years backend engineering experience with robust system design fundamentals and scalable AI infrastructure expertise.
Hands-on experience building production AI-powered backend systems, RAG pipelines, and agentic AI workflows.
Strong understanding of LLM limitations, hallucination mitigation, grounding, and AI observability/monitoring frameworks integrated into multi-disciplinary engineering teams.