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In-demand GenAI role, metro location, and mid-level experience increase applicant competition.
Specialized LLM and GenAI requirements limit industry portability despite transferable engineering skills.
Explicit 0–4 years plus required LLM, LangChain, Python, Databricks skills make shortlisting strict.
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Design and develop LLM-based AI solutions including chatbots, summarisation, and document intelligence for business use cases.
Build and optimise Retrieval Augmented Generation (RAG) pipelines involving data ingestion, embeddings, and retrieval processes.
Develop backend AI application services/APIs using Python frameworks (FastAPI, Flask, Streamlit) and integrate LLM solutions with enterprise systems and data sources.
0–4 years of total work experience with exposure to AI/ML, NLP, or Data Engineering projects.
Hands-on or strong learning exposure to LLM/GenAI use cases (projects, POCs, academic, or professional).
Strong Python/PySpark development skills with production-grade API integration experience.
Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or related field.
Experience with advanced LLM and GenAI technologies including Claude, OpenAI, LangChain, LangGraph, and agentic AI implementations.
Proficient in building and optimizing RAG pipelines, prompt engineering, and understanding LLM limitations and evaluation.
Background in data engineering integration with Fabric/Azure Databricks/Snowflake and good exposure to cloud platforms (Azure/AWS/GCP).