AI Principal Engineer, VP

Join us as a Principal Engineer

• This is a challenging role that will see you design and engineer software with the customer or user experience as the primary objective
• You’ll be playing a major role in the engineering centre of excellence, collaborating to improve the bank’s overall software engineering capability
• You’ll gain valuable stakeholder exposure as you build and leverage relationships, as well as the opportunity to hone your technical talents

What you'll do

As a Principal Engineer, you’ll be creating great customer outcomes via engineering and innovative solutions to existing and new challenges, and technology designs which are innovative, customer centric, high performance, secure and robust. You’ll be defining, creating and providing oversight and governance of engineering and design solutions with a focus on end-to-end automation, simplification, resilience, security, performance, scalability and reusability.

You’ll also be:

• Defining and developing architecture models and roadmaps of application and software components to meet business and technical requirements, driving common usability across products and domains
• Working with the various feature teams to produce software rapidly and of high quality which adds value to the business
• Determining the design and delivery approach for all of the platform’s software throughout its life
• Working within a platform or feature team along with software engineers to design and engineer complex software, scripts and tools to enable the delivery of bank platforms, applications and services, acting as a point of contact for solution design considerations

The skills you'll need

You’ll come with significant experience in software engineering, software or database design and architecture, as well as experience of developing software within a DevOps and Agile framework. We’d also like to see significant and demonstrable experience of implementing programming best practice, especially around scalability, automation, virtualisation, optimisation, availability and performance.

You’ll also need:

• A strong background in building and optimizing pipelines for LLMs, traditional ML models, and data processing pipelines using OSS frameworks or within the AWS/GCP ecosystems
• Strong experience in supervised or unsupervised learning, time‑series forecasting, optimization, feature engineering, model evaluation, and retraining strategies along with RAG, embedding pipelines, LLM orchestration, prompt engineering, and API integration
• Strong proficiency with Python
• Hands on engineering excellence in defining and enforcing standards for ML Ops, CI/CD, experiment tracking, model deployment, observability, security, and performance tuning
• The ability to evaluate and integrate capabilities across OpenAI/Anthropic models, fine‑tuning, GPU compute, AutoML, and feature stores
• A background of working with code repositories, bug tracking tools and wikis