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Senior, niche GenAI/LLM specialization reduces candidate density and competition.
Strong GenAI/LLM and data engineering specialization makes cross-industry fit limited.
Explicit 9–12 years plus mandatory GenAI/LLM and data engineering skills create high filter strictness.
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Architect and lead scalable enterprise-grade agentic and LLM solutions including multi-agent and tool-driven workflows.
Own end-to-end implementation and optimisation of RAG pipelines and retrieval systems ensuring performance and relevance.
Lead technical governance, mentor junior engineers, and influence GenAI strategy and architecture decisions across projects.
9–12 years total professional experience.
2–4+ years hands-on experience in LLM / GenAI delivery with production use cases.
Strong Python/Pyspark engineering expertise with production-grade API development experience.
Mandatory experience in data engineering (ETL/ELT, pipelines, orchestration) or NLP-focused data science/ML lifecycle.
Experienced in architecting and delivering GenAI systems using frameworks like LangChain or LangGraph and agent orchestration.
Proven ability to lead solution design or small teams with strong business problem to AI solution translation skills.
Familiarity with cloud platforms (Azure, AWS, GCP), data engineering tools (Databricks, Snowflake), and enterprise-grade system design patterns for GenAI.