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Early-mid AI roles attract many applicants, but LLM/agentic specialization narrows the pool.
LLM and GenAI skills transfer across industries but require specialized tooling and data experience, limiting fit somewhat.
Multiple mandatory LLM, Python, Databricks/Snowflake and integration requirements create stringent technical filters.
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Design and develop LLM-based AI solutions such as chatbots, summarisation, and document intelligence for business use cases.
Build and optimize Retrieval Augmented Generation (RAG) pipelines encompassing data ingestion, embeddings, and retrieval processes.
Develop backend services/APIs for AI applications using Python frameworks (FastAPI, Flask, Streamlit) and integrate LLM solutions with enterprise data sources, ensuring data quality and deployment readiness.
0–4 years total professional experience with exposure to AI/ML, NLP, or Data Engineering projects.
Hands-on or strong learning exposure to LLM/GenAI use cases including projects, proofs of concept, academic, or professional work.
Strong Python/PySpark development skills with API integration experience and working knowledge of cloud platforms (Azure, AWS, GCP).
Bachelor's or Master's degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or related field.
Experienced with LLMs including Claude, OpenAI, and frameworks like LangChain or LangGraph, with understanding of agent orchestration and tool-calling architectures.
Skilled in building and optimizing RAG pipelines and applying prompt engineering techniques including chaining and optimization.
Capable of handling large volumes of data, integrating with Fabric/Azure Databricks/Snowflake, and familiar with enterprise AI considerations such as data security and governance.