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Senior (9+ years) and highly specialized LLM/agentic skillset reduces applicant density despite metro location.
Highly domain-specific LLM/GenAI expertise and data engineering background required, limiting cross-industry transferability.
Explicit 9–12 years, mandatory LLM delivery experience and specific tech stack make filters stringent.
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Architect and lead enterprise-grade agentic and LLM-based GenAI solutions, defining scalable system design patterns and governance.
Lead end-to-end delivery and technical leadership across projects including building RAG pipelines, advanced prompt engineering, and production APIs.
Mentor junior engineers, conduct technical reviews, support pre-sales with architecture, and influence GenAI strategy and technology roadmap.
9–12 years total professional experience with 2–4+ years hands-on in LLM / GenAI production use cases.
Strong expertise with LLMs (Claude, OpenAI), GPT + Agentic AI implementation, RAG pipelines, LangChain or similar frameworks.
Proficient in Python/Pyspark for production-grade development, API integration, and experience with data engineering tools (e.g., Azure Databricks, Snowflake).
Prior experience in data engineering (ETL/ELT), data science/ML lifecycle especially NLP, or analytics engineering / data products.
Experienced technical leader who has led solution design or small teams delivering end-to-end GenAI systems.
Demonstrated ability to translate complex business problems into scalable AI/LLM solutions with focus on architecture governance and quality.
Strong domain knowledge in GenAI fundamentals including LLM limitations, evaluation, retrieval optimization, and cloud-based AI system integrations.