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Mid-level role with a common data title but GenAI specialization narrows the candidate pool, so medium competition.
Data engineering skills transfer across industries but GenAI/LLM specialization increases domain specificity, so medium sensitivity.
Explicit 4–6 years plus mandatory GenAI/LLM experience and strong tech stack requirements create high shortlisting strictness.
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Design and develop LLM-based applications including single-agent and simple multi-agent business use cases.
Build and maintain Retrieval-Augmented Generation (RAG) pipelines handling data ingestion, chunking, embeddings, retrieval, and response generation.
Develop backend APIs/services for AI applications using Python frameworks and integrate AI solutions with enterprise systems while ensuring data quality, deployment, and validation checks.
4–6 years total work experience with at least 1 year hands-on experience in GenAI / LLM-based applications.
Strong hands-on experience with LLMs (Claude, OpenAI), RAG pipelines, GPT + Agentic AI implementations, and frameworks like LangChain or LangGraph.
Proven production-grade Python/Pyspark engineering skills including API integration; experience with data engineering platforms such as Fabric, Azure Databricks, or Snowflake.
Prior experience in data engineering (ETL/ELT, pipelines), data science/ML lifecycle focusing on NLP, or analytics engineering/data products.
Experienced in designing and optimizing LAG pipelines and implementing agent orchestration with prompt engineering for enterprise AI solutions.
Comfortable working in production environments involving cloud platforms (Azure/AWS/GCP), containerization, CI/CD, and monitoring.
Demonstrates strong engineering depth with ability to handle large datasets and collaborate across Data Engineering and MLOps teams for scalable AI deployments.