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Niche GenAI skills reduce pool, but mid-level seniority and popular hiring amplify competition.
Highly domain-specific GenAI and data engineering requirements limit cross-industry transferability.
Explicit 6–9 years plus mandatory GenAI, Python/PySpark, Databricks/Snowflake, and production API experience.
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Design and develop LLM-powered applications using single and multi-agent agentic patterns for enterprise business use cases.
Build and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines including ingestion, embeddings, retrieval, orchestration, and response synthesis.
Develop production-grade GenAI APIs and services; integrate solutions with enterprise data platforms, workflows; apply guardrails and evaluation frameworks to ensure responsible AI usage.
6 to 9 years total professional experience.
1 to 3+ years of hands-on GenAI / LLM application development for production use cases.
Strong expertise in Python/PySpark, API development, and integration; experience with LLMs (Claude, OpenAI), RAG pipelines, LangChain or similar frameworks.
Experience with data engineering (ETL/ELT, pipelines, orchestration) and/or ML lifecycle especially NLP; exposure to cloud platforms (Azure/AWS/GCP) and data platforms like Fabric/Azure Databricks/Snowflake.
Experienced in implementing agentic AI including GPT and multi-agent orchestration with tool-calling architectures.
Comfortable working on large scale data pipelines and integrating LLM-based services into enterprise systems and workflows.
Skilled in prompt engineering, evaluation, and optimization strategies to reduce hallucinations and improve GenAI response quality.