Senior AI Data Engineer

Job Description: 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)

Responsibilities: 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)

Qualifications: 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)