Senior AI Data Engineer

Key Responsibilities Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis) Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs) Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications Integrate LLM solutions with enterprise systems, data platforms, and workflows Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling Contribute to reusable components, documentation, and engineering best practices Experience & Core Requirements (Must-Have) Overall Experience 6–9 years total experience 1–3+ years in hands-on GenAI / LLM application development (production use cases) 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 Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance) Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.) Exposure to agentic coding tools (e.g., Claude Code or similar environments) Key Responsibilities Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis) Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs) Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications Integrate LLM solutions with enterprise systems, data platforms, and workflows Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling Contribute to reusable components, documentation, and engineering best practices Experience & Core Requirements (Must-Have) Overall Experience 6–9 years total experience 1–3+ years in hands-on GenAI / LLM application development (production use cases) 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 Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance) Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.) Exposure to agentic coding tools (e.g., Claude Code or similar environments) Key Responsibilities Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis) Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs) Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications Integrate LLM solutions with enterprise systems, data platforms, and workflows Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling Contribute to reusable components, documentation, and engineering best practices Experience & Core Requirements (Must-Have) Overall Experience 6–9 years total experience 1–3+ years in hands-on GenAI / LLM application development (production use cases) 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 Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance) Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.) Exposure to agentic coding tools (e.g., Claude Code or similar environments)