Machine Learning Engineer at Investec

• 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