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GenAI demand boosts interest, but senior non-metro role with niche LLM/agent skills limits density.
Specialized GenAI production and LLM agent expertise moderately limits cross-industry fit.
Explicit 8–10 years plus mandatory LLM, RAG, vector DB, cloud, and infra skills make filters strict.
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Lead architecture and development of scalable AI-powered products using LLMs and Retrieval-Augmented Generation (RAG) technologies.
Design, build, deploy, and optimize end-to-end AI applications and agentic AI systems for business automation and intelligent decision-making.
Mentor engineering teams and establish AI architecture, engineering standards, and best practices.
8–10 years of overall software development experience with strong software engineering skills including distributed systems and API design.
3–4 years of experience designing and deploying production-grade Generative AI/LLM applications with commercial or open-source LLMs.
Hands-on expertise with RAG pipelines, vector databases (e.g., Pinecone, Weaviate, pgvector), and agentic AI frameworks (e.g., LangGraph, OpenAI Agents SDK).
Experience deploying and operating AI applications on cloud platforms (AWS, Azure, or GCP) with Docker, Kubernetes, and CI/CD pipelines.
Experienced in leading AI engineering initiatives that integrate with enterprise backend services and diverse databases.
Proficient in building and optimizing multi-agent AI workflows and AI applications focused on latency, reliability, and cost at scale.
Skilled in mentoring teams and shaping AI engineering standards while working cross-functionally with Product, Engineering, and Data teams.