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Niche LLM/agent skills reduce applicants, but mid-level lead title and popular AI domain raise competition.
Highly specialized LLM/agent skills and specific frameworks reduce cross-industry transferability.
Explicit 5–8 years, mandatory LLM experience and specific toolchain (LangChain, LangGraph, LangSmith) increase filter strictness.
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Design, develop, deploy, and maintain AI agents using LangChain, LangGraph, and LangSmith frameworks in production environments.
Build and optimize multi-agent workflows with features like memory, tool integration, human-in-the-loop, and Retrieval-Augmented Generation (RAG) pipelines.
Develop evaluation datasets, implement automated testing frameworks for LLM performance monitoring, and build dashboards to track latency, cost, quality, and hallucination rates.
5+ years of software engineering experience, with 2+ years in LLM/Generative AI application development.
Strong proficiency in Python and hands-on experience with LangChain, LangGraph, and LangSmith.
Experience with Databricks and MS SQL.
Work Experience Required: 5–8 years.
Has proven experience developing and deploying enterprise-scale, production-ready AI agent solutions with end-to-end lifecycle management: build, test, evaluate, deploy, and monitor.
Comfortable working with multi-agent orchestration workflows and integrating advanced AI capabilities such as vector databases and embeddings.
Familiar with domain-specific application preferably in Payments/Financial Services and understands AI model evaluation and LLMOps best practices.