AI Data Engineer

Job description

**Job Description: 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** **Responsibilities: 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** **Qualifications: 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*