AI Data Engineer

Key Responsibilities Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases Build and maintain RAG pipelines : data ingestion → chunking → embeddings → retrieval → response generation Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling) Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) Integrate AI solutions with enterprise systems, databases, and APIs Apply basic guardrails and validation checks to improve response quality and reduce hallucination Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation Collaborate with MLOps teams for deployment, monitoring, and iterative improvements Document solutions, reusable components, and best practices Must-Have Skills Experience 4–6 years total experience , with 1+ year hands-on experience in GenAI / LLM-based applications 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 / Preferred Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques Experience with evaluation of LLM outputs (quality, relevance, latency) Understanding of enterprise data privacy and security considerations in GenAI Exposure to Azure AI / Azure OpenAI / AI Search ecosystems Experience working on real client-facing AI solutions or POCs Key Responsibilities Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases Build and maintain RAG pipelines : data ingestion → chunking → embeddings → retrieval → response generation Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling) Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) Integrate AI solutions with enterprise systems, databases, and APIs Apply basic guardrails and validation checks to improve response quality and reduce hallucination Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation Collaborate with MLOps teams for deployment, monitoring, and iterative improvements Document solutions, reusable components, and best practices Must-Have Skills Experience 4–6 years total experience , with 1+ year hands-on experience in GenAI / LLM-based applications 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 / Preferred Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques Experience with evaluation of LLM outputs (quality, relevance, latency) Understanding of enterprise data privacy and security considerations in GenAI Exposure to Azure AI / Azure OpenAI / AI Search ecosystems Experience working on real client-facing AI solutions or POCs Key Responsibilities Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases Build and maintain RAG pipelines : data ingestion → chunking → embeddings → retrieval → response generation Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling) Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) Integrate AI solutions with enterprise systems, databases, and APIs Apply basic guardrails and validation checks to improve response quality and reduce hallucination Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation Collaborate with MLOps teams for deployment, monitoring, and iterative improvements Document solutions, reusable components, and best practices Must-Have Skills Experience 4–6 years total experience , with 1+ year hands-on experience in GenAI / LLM-based applications 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 / Preferred Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques Experience with evaluation of LLM outputs (quality, relevance, latency) Understanding of enterprise data privacy and security considerations in GenAI Exposure to Azure AI / Azure OpenAI / AI Search ecosystems Experience working on real client-facing AI solutions or POCs