We are looking for skilled and hands-on Agentic / Generative AI Engineers (4–6 years experience) to design, build, and deploy LLM-powered applications and agentic workflows for enterprise use cases such as document intelligence, knowledge assistants, summarisation, and workflow automation. This role is ideal for professionals transitioning from Data Engineering / Data Science backgrounds who have built or contributed to production-ready GenAI solutions. You will collaborate with cross-functional teams to deliver scalable, reliable, and business-driven AI solutions. Mandatory: Prior experience in Data Engineering or Data Science with strong understanding of data pipelines or ML workflows **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** **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**