Associate AI Data Engineer

Description Key Responsibilities - Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence). - Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval. - Implement prompt engineering techniques (prompt design, chaining, optimisation). - Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) . - Integrate LLM solutions with enterprise systems and structured/unstructured data sources. - Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations. - Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness . - Document solutions and contribute to reusable components and best practices. Must-Have Skills Experience - 2–4 years total experience , with exposure to AI/ML, NLP, or Data Engineering projects - Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional) LLM / GenAI & Agentic Engineering - Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.) - RAG pipelines and retrieval optimisation - GPT + Agentic AI implementation experience - Experience with: - LangChain, LangGraph, or similar frameworks - Agent orchestration and tool-calling architectures - Deep understanding of: - LLM limitations, evaluation, and optimisation strategies Core Engineering - Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience - Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring Data / AI Foundations (Mandatory) Prior experience in one or more: - Data Engineering (ETL/ELT, pipelines, orchestration) - Data Science / ML lifecycle (especially NLP) - Analytics engineering / data products Good-to-Have - Exposure to agentic workflows or tool calling concepts - Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure) - Experience with Azure OpenAI / Azure AI Search or similar stacks - Awareness of enterprise AI considerations (data security, privacy, governance) Responsibilities Key Responsibilities - Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence). - Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval. - Implement prompt engineering techniques (prompt design, chaining, optimisation). - Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) . - Integrate LLM solutions with enterprise systems and structured/unstructured data sources. - Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations. - Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness . - Document solutions and contribute to reusable components and best practices. Must-Have Skills Experience - 2–4 years total experience , with exposure to AI/ML, NLP, or Data Engineering projects - Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional) LLM / GenAI & Agentic Engineering - Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.) - RAG pipelines and retrieval optimisation - GPT + Agentic AI implementation experience - Experience with: - LangChain, LangGraph, or similar frameworks - Agent orchestration and tool-calling architectures - Deep understanding of: - LLM limitations, evaluation, and optimisation strategies Core Engineering - Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience - Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring Data / AI Foundations (Mandatory) Prior experience in one or more: - Data Engineering (ETL/ELT, pipelines, orchestration) - Data Science / ML lifecycle (especially NLP) - Analytics engineering / data products Good-to-Have - Exposure to agentic workflows or tool calling concepts - Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure) - Experience with Azure OpenAI / Azure AI Search or similar stacks - Awareness of enterprise AI considerations (data security, privacy, governance) Qualifications Key Responsibilities - Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence). - Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval. - Implement prompt engineering techniques (prompt design, chaining, optimisation). - Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) . - Integrate LLM solutions with enterprise systems and structured/unstructured data sources. - Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations. - Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness . - Document solutions and contribute to reusable components and best practices. Must-Have Skills Experience - 2–4 years total experience , with exposure to AI/ML, NLP, or Data Engineering projects - Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional) LLM / GenAI & Agentic Engineering - Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.) - RAG pipelines and retrieval optimisation - GPT + Agentic AI implementation experience - Experience with: - LangChain, LangGraph, or similar frameworks - Agent orchestration and tool-calling architectures - Deep understanding of: - LLM limitations, evaluation, and optimisation strategies Core Engineering - Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience - Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring Data / AI Foundations (Mandatory) Prior experience in one or more: - Data Engineering (ETL/ELT, pipelines, orchestration) - Data Science / ML lifecycle (especially NLP) - Analytics engineering / data products Good-to-Have - Exposure to agentic workflows or tool calling concepts - Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure) - Experience with Azure OpenAI / Azure AI Search or similar stacks - Awareness of enterprise AI considerations (data security, privacy, governance)l