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)