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Niche GenAI requirements reduce broad applicant pool but mid-level appeal increases competition.
Role requires specialized LLM, RAG and data engineering skills, making cross-industry transferability limited.
Explicit 2–4 years plus mandatory LLM, LangChain, Databricks and cloud skills create strict filters.
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Design and develop LLM-based AI solutions such as chatbots, summarization, and document intelligence for business use cases.
Build and optimize RAG pipelines including data ingestion, embeddings, and retrieval while implementing prompt engineering techniques.
Develop and integrate backend APIs for AI applications using Python frameworks, ensuring data quality, model performance, and deployment readiness.
2–4 years total professional experience with exposure to AI/ML, NLP, or Data Engineering projects.
Hands-on experience or strong exposure to LLM/GenAI use cases including projects, proofs of concept, academic work, or professional engagements.
Strong Python/Pyspark engineering skills with proven experience in production-grade development and API integration.
Prior experience in one or more of Data Engineering (ETL/ELT, pipelines, orchestration), Data Science/ML lifecycle (especially NLP), or Analytics engineering/data products.
Experienced with LLMs (Claude, OpenAI) and skilled in RAG pipeline design, retrieval optimization, and GPT plus Agentic AI implementations.
Proficient in LangChain, LangGraph, or similar agent orchestration and tool-calling frameworks with a deep understanding of LLM limitations and optimization strategies.
Familiar with cloud platforms (Azure/AWS/GCP), data engineering tools like Fabric/Azure Databricks/Snowflake, and CI/CD methodologies for AI deployments.