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LLM specialization narrows applicants despite mid-level metro role and a generic Python engineer title.
Role requires specialized LLM and agent engineering experience, limiting cross-industry transferability.
Explicit years plus mandatory LLM, LangChain, vector DB and cloud deployment requirements create strict filters.
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Design, develop, and deploy AI-powered applications including agents, copilots, and autonomous workflows using frameworks like LangChain and AutoGen.
Build and integrate RAG solutions, semantic search, and knowledge assistant systems with enterprise APIs and databases.
Package, deploy, and monitor AI applications in cloud environments while contributing reusable frameworks and AI engineering best practices.
3–8 years of overall software engineering experience.
Minimum 2 years of hands-on experience in Generative AI, Agentic AI, or LLM-based application development.
Strong proficiency in Python and experience with REST APIs and frameworks like FastAPI or Flask.
Bachelor of Technology degree.
Experienced in developing LLM-powered applications with practical knowledge of agent workflows, prompt engineering, and RAG architectures.
Capable of cross-functional collaboration with engineering, product, and business stakeholders and effective communication of technical concepts.
Experienced in deploying AI solutions on cloud platforms (Azure/AWS/GCP) and applying CI/CD and containerization (Docker) best practices.