AI Engineer - Databricks (gn)

Purpose of job

The role encompasses the design and delivery of enterprise-scale Generative AI (GenAI) and Machine Learning (ML) solutions that support operational processes within the Asset Management business. The position is hands-on and development-focused, spanning from initial prototyping through to scalable production deployment, embedded within an experienced team of senior engineers and solution architects. The position also collaborates closely with Operations teams, subject matter experts and technology partners in order to translate business requirements into secure and scalable AI solutions.

Tasks

• Design, develop and deliver full-stack GenAI and ML applications, including data pipelines, embedding and retrieval workflows, and evaluation frameworks for LLM and RAG-based systems.
• Implement AI components in accordance with defined architectural standards, with a strong focus on reusability and scalability.
• Architect and deliver robust integration layers connecting AI components with enterprise platforms and operational workflows.
• Support prototype validation and the transition of proven AI solutions into stable production environments.
• Collaborate closely with senior engineers, architects and subject matter experts to shape solution designs and actively contribute to the development of AI agents.
• Drive documentation, testing and knowledge sharing to maintain high delivery standards across the team.

Requirements

• Academic background or equivalent qualification in Computer Science, Data Science, Engineering or a related discipline.
• Between 2 and 5 years of experience in software engineering or full-stack application development, including at least 1–2 years of practical exposure to ML or GenAI solutions, gained within agile team environments.
• Solid command of Python and associated ML and GenAI frameworks, alongside demonstrable experience with large language models, RAG-based architectures, embedding techniques and prompt engineering.
• Proven expertise in contemporary web technologies — React, TypeScript and Node.js — with the ability to independently deliver across both front-end and back-end layers.
• Hands-on exposure to vector databases, cloud-native engineering practices (including Docker, Kubernetes and Azure) as well as relational database systems.
• Working knowledge of DevOps and agile toolchains, complemented by the ability to design and implement automated testing across unit and integration levels.
• Effective communicator with a strong capacity to bridge business requirements and technical implementation, making complex concepts accessible to non-technical audiences.