AI Engineer

Role Purpose

We are seeking a highly motivated AI Engineer to design, build and deploy scalable AI solutions across Business Transaction Banking.

The successful candidate will combine strong software engineering skills with practical expertise in AI frameworks, cloud-native platforms, data integration and modern AI engineering practices.

The role will contribute to initiatives including

Generative AI and Large Language Model solutions

Agentic AI systems and multi-agent workflows

Retrieval Augmented Generation, embeddings and vector search

Conversational AI agents and knowledge management platforms

Intelligent document processing and workflow automation

Machine learning products and AI-powered decision support

Key Responsibilities

AI Solution Development

Design, develop and deploy AI-powered applications and services.

Build scalable agentic AI solutions using modern orchestration frameworks.

Develop RAG pipelines using vector embeddings, semantic search and knowledge retrieval patterns.

Implement conversational AI assistants and intelligent workflow automation.

Integrate AI models with enterprise systems, APIs and data platforms.

Develop prompt engineering, evaluation and guardrail frameworks.

Machine Learning and LLM Engineering

Train, fine-tune and optimise machine learning and generative AI models where appropriate.

Build model evaluation and monitoring capabilities.

Develop model serving and inference pipelines.

Implement responsible AI controls and validation processes.

Evaluate emerging AI technologies and recommend adoption opportunities.

Platform and Cloud Engineering

Build and deploy solutions on Google Cloud Platform.

Develop containerised applications using Docker and Kubernetes.

Work with GKE, Vertex AI, BigQuery, Cloud Run and related GCP services.

Implement CI/CD pipelines and automated testing frameworks.

Ensure scalability, resilience, security and observability of production services.

Data and Integration

Integrate AI solutions with structured and unstructured data sources.

Build ingestion pipelines from SharePoint, APIs, databases and cloud storage.

Develop vector stores and embedding pipelines.

Work closely with Data Engineers to optimise data access patterns.

Innovation and Collaboration

Create rapid proofs of concept and MVPs.

Contribute to reusable AI frameworks, accelerators and delivery patterns.

Collaborate with Product Owners, SMEs, Architects and Engineers.

Present technical solutions to business and senior stakeholders.

Mentor junior engineers and contribute to AI capability growth across the lab.

Essential Skills and Experience

Programming and Software Engineering

Strong Python development experience.

Good understanding of software engineering best practice.

Experience building REST APIs and microservices.

Knowledge of Git, version control and peer review processes.

API design, integration, security and authentication patterns.

AI and Generative AI

Experience working with Large Language Models.

Practical experience with LangChain, LangGraph or comparable agent frameworks.

Prompt engineering, AI evaluation and guardrail design.

Understanding of embeddings, vector databases and semantic search.

Knowledge of RAG architecture and contextual retrieval patterns.

Cloud, DevOps and Production Engineering

Experience with GCP, Azure or AWS.

Containerisation using Docker.

Kubernetes deployment experience.

CI/CD pipeline implementation.

Understanding of scalable, resilient and observable cloud-native services.

Data Engineering

Experience working with structured and unstructured datasets.

SQL and data modelling knowledge.

Data pipeline development experience.

Understanding of BigQuery or equivalent analytics platforms.

Desirable Skills

Vertex AI and Gemini models.

OpenAI, Anthropic or comparable LLM APIs.

MLflow, MLOps practices, feature stores or model registries.

Databricks, Spark, Beam or large-scale data processing.

TensorFlow or PyTorch.

Knowledge graphs, Document AI, OCR or intelligent document processing.

React or TypeScript front-end development.

Financial Services, Payments or Transaction Banking knowledge.

Behavioural Competencies

Strong problem-solving mindset and curiosity for emerging AI technologies.

Passion for innovation, experimentation and rapid learning.

Excellent communication skills with technical and non-technical audiences.

Ability to work in ambiguity and fast-moving delivery environments.

Growth mindset, ownership and accountability for outcomes.

Collaborative working style across business, product and technology teams.

Success Measures

Deliver production-ready AI capabilities that create measurable business value.

Build reusable AI patterns that can be adopted across BTB.

Improve delivery productivity through automation and agentic workflows.

Contribute to AI foundations and platform capabilities.

Support successful deployment and operation of AI solutions in production.

Core Technology Stack

Technology / Capability

Expected Application

Python

Primary AI engineering and orchestration language

LangChain / LangGraph

Agent orchestration, multi-step workflows and RAG patterns

Vertex AI / Gemini

Model development, evaluation and enterprise AI capabilities

BigQuery

Analytical data platform and AI context source

GCP

Cloud-native hosting and managed AI services

Docker and Kubernetes / GKE

Containerised deployment and scaling

FastAPI

API services for AI applications and agents

Vector databases / embeddings

Semantic search and knowledge retrieval

React / TypeScript

Front-end interfaces for AI tools where needed

Role Positioning

This role is best positioned as an AI Engineer or Agentic AI Engineer role rather than a traditional Data Scientist role. The emphasis is on engineering production-ready AI products, reusable AI platforms, enterprise integration and safe deployment of AI capabilities within a regulated environment.