Andrea Head
ML Platform Architect & Technical Lead
I define the architecture and technical strategy for production AI/ML systems.
About
I define the architecture and technical strategy for production AI/ML systems.
My path from mathematician to lecturer to ML engineer to technical lead gives me a rare combination: the mathematical depth to derive ML theory from first principles and the engineering discipline to ship it reliably.
I hold an MSc in Applied Mathematics with specialisation in Numerical Analysis, Control Theory, Optimisation, and Mathematical Epidemiology from North-West University. I am currently pursuing a BA in Philosophy at UNISA, because the people building AI should be the ones questioning it.
Over a decade, I have delivered end-to-end ML across FinTech, banking, and retail, building production GenAI/RAG systems under real compliance and privacy constraints, scaling MLOps platforms, and leading teams that own P&L-adjacent outcomes.
I speak native Afrikaans and English, with Dutch, Mandarin, German, and Spanish in progress.
Core strengths
- Production GenAI and RAG delivery
- MLOps, evaluation, and deployment automation
- AI platform engineering and cloud architecture
- Governed, privacy-conscious AI systems
- Technical leadership and platform strategy
Commercial signals
- £40M+ first-year ROI from standardising and stabilising an enterprise AI platform, enabling high-impact forecasting and personalised marketing models
- Prediction Factory scaled to 50+ models per day
- ~90% reduction in P1/P2 incidents
- 70% faster experiment turnaround
- £2M+ funding round contribution at JUMO
Mentorship & capability building
Making other engineers better.
I actively mentor engineers navigating the transition from data science to production ML engineering; the "last mile" gap that determines whether AI work delivers real value. My mentorship focuses on MLOps principles, system architecture thinking, CI/CD discipline, and designing for deployment and maintainability.
How I think about engineering
Strong systems beat clever demos.
I care about explicit interfaces, observability, deterministic workflows where possible, and architectures that make failure states easier to understand. For GenAI and RAG work, that means grounding, validation, fallbacks that are visible rather than magical, and deployment patterns that respect privacy and operational constraints.
For machine learning platforms, it means reducing toil, increasing safe throughput, and building the tooling that lets teams ship confidently instead of heroically.
I operate as a force multiplier. My goal is not just to deliver my own work well, but to raise the technical bar for the entire team. That means building the platforms and patterns that let others ship confidently, establishing governance standards that make compliance a default rather than a burden, and mentoring engineers who go on to lead their own initiatives.
Third-party perspective
What colleagues and leaders say
"Andrea's contribution extends far beyond the role of a Senior MLOps Engineer; he has functioned as a critical AI Product and Technical Leader. His distinction lies not just in his engineering capability, but in his proven ability to convert 'impossible' enterprise constraints into novel, production-ready technical solutions."
"Andrea operates with the strategic mindset of a Lead Architect. He possesses the rare ability to bridge the gap between 'Scientific Rigour' and 'Operational Speed'."
"Andrea is a standout technical leader within our practice. His work consistently sets the standard for technical excellence, and he operates as a force multiplier; his presence raises the technical bar for the entire team."
"It is rare to find technical talent that can so effectively translate complex analytical requirements into stable, high-scale production systems."
"Andrea did not simply teach me technical skills; he taught me how to think like an architect. Because of his mentorship, I was promoted to AI Adoption Lead — a transition that typically takes two to four years, achieved in just 12 months."
Experience
Career timeline
Equal Experts — ML Engineer (Technical Lead)
February 2024 – present
John Lewis Partnership
- Deployed a production-grade RAG chatbot ("Drummie") in 3 weeks with a novel PII-redaction layer on Google Cloud Run — 90% adoption in week one
- £40M+ first-year ROI from spearheading forecasting (bakery demand) and personalised promotional marketing models, enabled by platform stabilisation work
- ~90% reduction in P1/P2 incidents through MLOps standardisation
- Defined GCP "paved road" AI Platform architecture; 40 AI products deployed in 5 months
- Built an offline-first CodeGemma implementation for secure local documentation generation
Médecins Sans Frontières (pro bono, Feb–Mar 2024)
- Productionised ML models for epidemic modelling in remote/low-resource environments
JUMO — Senior ML Engineer (MLOps)
April 2022 – January 2024
- Technical Product Owner for the Prediction Services Team
- Scaled model training from ~1 model/week to 50+ models/day via pipeline automation
- 70% faster experimentation through generalised champion model architectures
- Drift-detection latency cut from weeks to minutes with real-time monitoring
- Architecture was a key asset during due diligence, contributing to a £2M+ funding round
JUMO — ML Engineer
June 2021 – April 2022
- Automated scorecard deployment pipelines for credit risk models in a cloud-native environment
- Engineering bridge between Decision Science and production: translated maths into scalable code
Praelexis — ML Engineer
October 2016 – June 2021
- Investec: Intelligent transaction categorisation engine — reduced manual labelling by 60%
- Capitec: Co-led deployment of a first-of-its-kind live SMS data feature at high-velocity scale
- Cloud leadership during Capitec's "Best Bank in the World" period (Lafferty Group)
- Security hardening: migrated legacy services to Azure, established governance best practices
North-West University — Lecturer & Researcher
January 2014 – September 2016
- Lectured Linear Algebra, Mechanics, Mathematical Modelling, and Optimisation
- Multidisciplinary research with biologists and statisticians (Mathematical Biology / Epidemiology)
Skills
Technical arsenal
GenAI & LLM Engineering
- RAG system design and implementation
- LLM-powered application development (local, offline, and cloud)
- Prompt and tool orchestration
- PII-redaction patterns for compliant LLM use
- Production GenAI considerations (governance, security, privacy)
MLOps & ML Platform Engineering
- ML lifecycle management and automation
- Model monitoring, drift detection, and alerting
- CI/CD for ML (training pipelines, deployment pipelines)
- Experiment tracking and lifecycle management (MLflow, Dataiku)
- Model governance and risk frameworks
Cloud & Infrastructure
- GCP (Cloud Run, Vertex AI, GCS)
- AWS and Azure
- Docker and Kubernetes
- Serverless architecture patterns
Software Engineering
- Python (primary)
- TDD, DDD
- REST API design
- Version control and code review practices
Mathematics & Research
- Applied Mathematics (Numerical Analysis, Control Theory, Optimisation)
- Mathematical Epidemiology / Mathematical Biology
- Statistical modelling
- Ability to derive and explain ML theory from first principles
Leadership & Strategy
- Technical lead / TPO experience
- Architecture and platform strategy
- Mentorship and capability building
- Stakeholder engagement and strategic alignment
- Agile delivery
Education
Academic credentials
| Qualification | Institution | Year | Note |
|---|---|---|---|
| BA Philosophy | UNISA | 2025–present | Intentional — AI Ethics |
| MSc Applied Mathematics | North-West University | 2013–2015 | cum laude |
| BScHons Applied Mathematics | North-West University | 2012 | cum laude, Best Student in Mathematics |
| BSc Computer & Mathematical Science | North-West University | 2007–2011 | cum laude |
Because the people building AI should also be questioning it.
Blog & Philosophy Corner
Where ML engineering practice meets AI ethics
I write about the intersection of production AI systems and the philosophical questions they raise — from MLOps and deployment discipline to governance, fairness, and the responsibilities of the people who build these systems.
Read my blogNext steps
Explore the work behind the claims.
If you want depth rather than slogans, the best next step is to review a few case studies and articles.