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Mid-level GenAI role in a metro with broad skillset and moderate brand strength.
Skills are transferable across industries but require specific LLM and data-engineering experience, giving moderate sensitivity.
Explicit 2–4 year requirement plus mandatory LLM, RAG, Python, and data engineering skills raises filtering strictness.
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Design, develop, and optimise LLM-based AI solutions including chatbots, summarisation, document intelligence, and RAG pipelines.
Develop backend services/APIs for AI applications using Python frameworks like FastAPI, Flask, or Streamlit, integrating LLM solutions with enterprise data sources.
Apply guardrails and evaluation to improve AI response quality and collaborate across teams for data quality, model performance, and deployment readiness.
2–4 years total experience with exposure to AI/ML, NLP, or Data Engineering projects.
Hands-on experience or strong exposure to LLM/GenAI use cases, including RAG pipelines, GPT + Agentic AI, and frameworks such as LangChain or LangGraph.
Strong Python/Pyspark engineering skills with production-grade API integration experience.
Prior experience in at least one of the following: Data Engineering (ETL/ELT), Data Science/ML lifecycle (especially NLP), or Analytics engineering/data products.
Experienced in building and optimising complex LLM and RAG-based solutions with agent orchestration and prompt engineering expertise.
Capable of handling large-scale data sets with strong integration skills on platforms like Azure Databricks, Fabric, and Snowflake and familiar with cloud services (Azure/AWS/GCP).
Proficient in deploying production-grade AI applications with deep understanding of LLM limitations, evaluation, and optimisation strategies, along with awareness of enterprise AI considerations like data security and governance.