The PB Data team serves as a strategic partner within our Private Bank business, managing the full lifecycle of data and machine learning capabilities — from data ingestion and feature engineering to model deployment, integration, and continuous improvement.
A core component of this capability is the Insights Engine, which is being established as the central ML and AI platform across the bank. The Insights Engine enables the scalable delivery of intelligent decisioning, supporting the bank's digital and growth strategy.
The business strategy incorporates key objectives such as:
Growth (client acquisition and entrenchment, particularly in the Affluent segment)
Enhanced client engagement (personalised digital experiences and intelligent decisioning)
Cost reduction (automation and optimisation of servicing through digital channels)
From a technology perspective, the organisation is transitioning into the cloud, with a strong focus on:
Building all new ML capabilities in cloud-native environments
Industrialising ML through platform, MLOps, and integration patterns
Embedding governance and Responsible AI into delivery
Role Overview
The primary expectation of this role is to design, build, deploy, and operate machine learning systems that are scalable, reliable, and integrated into business and digital processes.
This role focuses on:
Building ML pipelines and platform components
Enabling model deployment and integration
Ensuring training-to-inference consistency
Embedding monitoring, governance, and continuous learning
Operating within a high-autonomy environment, you will:
Own and deliver end-to-end ML engineering solutions
Collaborate across Data, Engineering, and Architecture teams
Contribute to making the Insights Engine the default ML delivery platform across the bankDesign, build, test, and enhance scalable ML pipelines and systems aligned to business objectives
Develop and maintain Feature Store capabilities, enabling feature reuse and consistency
Implement CI/CD pipelines for machine learning models
Manage model deployment patterns (batch and real-time) using cloud infrastructure
Ensure training-to-inference consistency and reproducibility of models
Implement model monitoring, drift detection, and alerting mechanisms
Build and support automated retraining pipelines and continuous learning frameworks
Enable seamless integration of ML outputs into digital applications and business processes
Collaborate with Data Scientists to operationalise models into production
Work with Engineering teams to ensure scalable and reliable integration into digital channels
Contribute to ML governance frameworks, including Responsible AI practices (fairness, explainability, bias detection)
Support the design and evolution of the Insights Engine architecture and operating model
Drive automation and standardisation across the ML lifecycle
Evaluate and adopt new tools and technologies to improve platform capability
Proactively manage delivery timelines and technical execution
Engage stakeholders to ensure alignment with business priorities and strategy
Contribute to knowledge sharing and capability uplift across the organisation
Minimum Qualifications and Knowledge
A degree in Computer Science, Engineering, Mathematics, or a related field
5+ years of experience in Machine Learning Engineering / MLOps
Relevant cloud certifications (e.g. AWS, Azure) in cloud environments i.e AZ900, AI300,AI901
Experience building, deploying, and operating machine learning models in production
Strong proficiency in Python, SQL, and PySpark
Experience with cloud platforms (preferably Azure ML, Databricks, Azure DevOps)
Experience with CI/CD pipelines and automation frameworks
Understanding of:
Feature Store concepts
Model lifecycle management
Data engineering and distributed systems
Competencies
Strong systems thinking — ability to design scalable platform solutions
Ability to translate business problems into technical ML solutions
Strong collaboration across cross-functional teams (Data, Engineering, Architecture)
Excellent communication skills (technical and non-technical)
High ownership and accountability for delivery
Ability to work in a fast-paced, evolving environment
Strong problem-solving and analytical thinking skills
Focus on building reusable and scalable solutions
Understanding of governance, compliance, and Responsible AI principles
Attention to detail and quality of delivery
Self-starter with the ability to operate independently
Continuous learning mindset