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# Data Engineer/GenAI Developer

- **Company:** Devonshire Hayes
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Systems, Python (Programming Language), Standard Sql, Search Technologies, Software Deployment, Unstructured Data, Large Language Models, Snowflake, Apache Spark, Git, Build Management, Microsoft Fabric, AI Platforms, Apache Kafka, Machine Learning Operations, Restful APIs, Data Pipelines, Docker, Databricks, Microservices
- **Published:** August 26, 2026
- **Apply:** https://www.careerboard.com/pt/en/find-jobs-in-United-Kingdom/-333C4F466BF04B61F8/

## About the Role

6-8 years' hands-on data engineering experience with strong Python and SQL skills.

Strong ETL/ELT and large-scale pipeline experience using Spark, Databricks, Kafka, Airflow or equivalent technologies.

Commercial experience with AWS, Azure or GCP and modern platforms such as Databricks, Snowflake or Microsoft Fabric.

Practical GenAI experience across LLMs, RAG, embeddings, vector databases and frameworks such as LangChain, LlamaIndex or Semantic Kernel.

Strong REST API, microservices, Docker, Git and CI/CD engineering practices, with experience deploying solutions into production.

A sound understanding of data quality, governance, security and responsible AI.

Useful additional experience:

Azure OpenAI, AWS Bedrock or Vertex AI; LLMOps/MLOps, evaluation and observability; agentic AI; complex enterprise or Legacy integration; financial services or insurance.

## Description

We are seeking an experienced Data Engineer to design and build modern data solutions supporting enterprise AI and Generative AI use cases. This is a hands-on engineering position spanning trusted data pipelines, RAG and LLM services, APIs and production deployment.

What you'll do:

Design and optimise scalable ETL/ELT pipelines for structured, semi-structured and unstructured data.

Build production-ready RAG pipelines covering ingestion, chunking, embeddings, vector search, retrieval and grounding.

Integrate enterprise data with LLM and AI services through secure, reusable APIs and components.

Take AI use cases from prototype through production, including testing, CI/CD, monitoring and troubleshooting.

Work with data scientists, AI engineers, architects and business stakeholders to deliver measurable outcomes.


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