Senior Machine Learning Engineer

Key Focus Areas 

• ML Solution Design and Delivery (End-to-End Engineering) 

• Strategic Alignment and Use-Case Shaping 

• MLOps (Machine Learning Operations), Deployment, and Operational Excellence 

• Governance, Security, Privacy, and Responsible ML 

• Cross-Functional Collaboration and Product Enablement  

• Technical Enablement, Standards, and Capability Building 

Key Accountabilities 

1) ML Solution Design and Delivery (End-to-End Engineering) 

• Design, build, and deploy machine learning models that improve prediction, classification, optimisation, or personalisation outcomes to deliver measurable business value. 

• Translate business requirements into ML system designs, defining features, modelling approaches, evaluation criteria, and integration patterns. 

• Engineer reusable ML components and services that accelerate delivery across multiple use cases and markets. 

2) Strategic Alignment and Use-Case Shaping 

• Collaborate closely with the Manager: New Tech and AI to align machine learning initiatives with Group priorities, HC transformation objectives, and innovation goals. 

• Contribute to roadmap planning by sizing effort, dependencies, risks, and value, and by recommending fit-for-purpose ML approaches and tooling. 

• Ensure ML initiatives are designed with scale, sustainability, and cross-market applicability in mind. 

3) MLOps, Deployment, and Operational Excellence 

• Implement MLOps practices that enable consistent packaging, testing, release, and rollback of models using approved engineering and security standards. 

• Monitor and maintain the performance of deployed ML models, implementing updates and improvements based on drift detection, data analysis, and user feedback. 

• Build and operate model observability, including performance dashboards, alerting, lineage, and incident response runbooks to ensure service reliability. 

5) Governance, Security, Privacy, and Responsible ML 

• Support data governance initiatives by ensuring ML pipelines, datasets, and outputs comply with data management policies, access controls, and retention requirements. 

• Implement model documentation, traceability, and validation controls that support model risk management, audit readiness, and regulatory expectations. 

• Embed responsible ML practices by testing for bias, fairness, explainability, robustness, and appropriate use, particularly for people and customer-impacting solutions. 

6) Cross-Functional Collaboration and Product Enablement 

• Collaborate with cross-functional teams, including product development and innovation, to leverage machine learning solutions in new product offerings and enhancements. 

• Partner with Group CoEs to align to enterprise standards, reference architectures, and reusable platforms while accelerating delivery through shared patterns. 

• Integrate with HC Services and HC Business Partnering teams to ensure ML solutions improve workforce decisions, employee experience, service efficiency, and change adoption. 

7) Technical Enablement, Standards, and Capability Building 

• Stay informed about emerging technologies and best practices in ML engineering, MLOps, and data engineering, and apply them pragmatically to improve outcomes. 

• Provide technical support and training to team members on ML engineering tools, coding standards, and delivery methodologies. 

• Contribute to playbooks, templates, and engineering guardrails that improve consistency, quality, and time-to-value across ML initiatives. 

Knowledge & Skills 

• Machine learning engineering and applied model development in production environments.  

• MLOps practices, model monitoring, and ML lifecycle management in regulated contexts.  

• Data engineering concepts including pipelines, orchestration, data quality, and metadata.  

• Responsible AI, privacy-by-design, and model risk management controls.  

• Cloud-native engineering patterns and enterprise integration approaches.  

• Banking/financial services operating complexity across multiple African jurisdictions. 

Skills 

• Strong software engineering capability for ML systems (design, testing, CI/CD, reliability).  

• Feature engineering and dataset curation, including robust validation and quality checks.  

• Model evaluation, performance optimisation, drift detection, and retraining strategies.  

• Stakeholder engagement and translation of business needs into technical solutions.  

• Problem solving in complex environments with multiple dependencies and constraints.  

• Coaching and knowledge sharing to uplift capability in a progressive HC function. 

Qualifications & Experience  

Education / Qualification 

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field.  

• Postgraduate qualification (advantageous) in Machine Learning, Data Science, AI, or Software Engineering.  

• Relevant certifications (advantageous): Cloud ML/Engineering (e.g., Azure/AWS/GCP), data engineering, security, or Responsible AI. 

Work Experience 

• 7 + years experience, including a track record of 4 years in a technical position or team management.   

• Exposure to client service and quality management is preferred.  

Technical Competencies 

• Python and production ML frameworks (e.g., scikit-learn, PyTorch/TensorFlow) with clean coding practices.  

• MLOps tooling and patterns (CI/CD for ML, model registries, experiment tracking, model monitoring).  

• Data pipeline engineering (SQL, orchestration concepts, distributed processing concepts, data quality checks).  

• API/service deployment patterns (containerisation concepts, service integration, authentication/authorisation).  

• Model explainability, fairness testing, robustness checks, and governance documentation practices.  

• Secure engineering practices aligned to enterprise standards (secrets handling, encryption concepts, access control). 

Behavioural Competencies 

• Strategic thinking with a delivery mindset and strong accountability for outcomes.  

• Collaboration and influence across Group CoEs, HC Services, and HC Business Partnering teams.  

• Sound judgement and disciplined execution in a risk-managed, regulated environment.  

• Learning agility and curiosity balanced with pragmatism and operational stability.  

• Clear communication that simplifies complex technical topics for non-technical stakeholders.  

• Integrity and ethical orientation, especially when building people- or customer-impacting models.