Data Science Engineer

The role: Building generative AI solutions that go into production and stay there, for an organisation serving millions of customers. You work directly with internal customers to understand how their processes actually run, then design, build and deploy the AI systems that improve them. The work has a second dimension: once a solution works for one use case, you turn it into a reusable building block that other parts of the organisation can pick up.

Mostly greenfield, very little legacy.

The environment: Our client is an international bank, and you would join a team driving GenAI adoption across a wide range of business domains. Solutions are co-created with the stakeholders who will use them, not handed over at the end. It is a cross-functional setup with engineering, product and business colleagues in the same team, and a culture built around fast learning, initiative and short feedback loops. Responsible and cost-effective AI is an explicit requirement here, not an afterthought.

What you'll do: Design, build, test, deploy and run scalable GenAI solutions. Work directly with internal customers to learn how their processes and systems work. Co-create solutions using the reusable GenAI building blocks the team has developed. Convert use-case-specific solutions into reusable ones that work across domains. Take accountability for building responsible and cost-effective AI at scale. Solve complex problems together with team members and end users.

Tech stack: Core: Python · LLMs and NLP · generative AI applications · LLMOps / MLOps · Azure or comparable cloud · Git and CI/CD.

Also in the mix: containerisation · API development and integration · ETL · relational and vector databases.

What you bring: Must-haves: Three or more years as a data scientist or in a comparable role. Strong Python, with the ability to write clean, reusable code. A solid understanding of NLP and LLMs, ideally including building generative AI applications. Experience developing, evaluating, deploying and monitoring models, with LLMOps, MLOps or DevOps practice in cloud production environments.

Engineering breadth: Git, CI/CD, ETL, a cloud platform such as Azure, API development, containerisation, and relational and vector databases. Product thinking. You translate a business problem into a data science solution that is pragmatic rather than overengineered.

Nice to have: A software engineering background. Experience leading technical projects, coaching others and setting best practice. You are curious, you like working with the people who will use what you build, and you can explain a complex idea simply enough to build trust outside your own discipline. You care about shipping clean, maintainable, testable work rather than impressive notebooks.