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Tier-1 brand plus mid-level experience increases competition, but niche LLM/RAG expertise narrows the candidate pool.
Specialized LLM, RAG, and agent engineering skills make cross-industry transferability limited and domain-sensitive.
Explicit 4–7 years and many mandatory LLM, RAG, backend, and production requirements create strict shortlisting filters.
Develop and maintain scalable backend services and APIs incorporating AI and large language model (LLM) capabilities for web, mobile, and enterprise applications.
Build, integrate, and operate production-grade LLM APIs and Retrieval-Augmented Generation (RAG) pipelines including ingestion, embedding, indexing, and retrieval.
Design and manage AI workflow orchestration, agentic multi-step reasoning systems, and observability frameworks focused on performance, cost, and reliability optimization.
Proven backend engineering skills with expertise in Python, Node.js, Java, or Go and API design (REST/GraphQL).
Production experience integrating with LLM APIs such as OpenAI, Anthropic, Gemini, or Azure OpenAI.
Experience with RAG architectures, embedding techniques, vector databases, and AI orchestration tools like LangGraph, n8n, or Temporal.
Work Experience Required: 4–7 years of backend engineering experience with system design fundamentals.
Experienced in building AI-powered backend systems and agentic workflows deployed in production environments.
Strong understanding of LLM operational challenges including hallucination mitigation, grounding, cost vs. performance tradeoffs, and AI observability.
Comfortable working in structured, cross-functional teams delivering scalable enterprise-grade AI infrastructure and APIs.