Selected work

Building the foundations that help enterprise AI move beyond the demo.

These examples explain the capability, responsibility and lessons behind my work without disclosing confidential organisations, projects, commercial values or technical architecture.

10+years across enterprise software, data and applied AI
Securityturning AI risk into practical engineering standards
Private AIevaluating models in controlled enterprise environments
Globalguiding technical teams across regions and disciplines

A note on confidentiality

The details below are intentionally generalised. They describe the kind of problems I have worked on, the responsibility I carried and what I learned—without exposing project names, system designs, internal data or commercial information.

Case 01 · Security by design

Creating a safer way to ship AI

Created enterprise AI security standards and automated adversarial testing that helps teams find weaknesses before production.

The problem

Teams moving from AI experiments toward real products needed a consistent way to identify prompt-injection, information-exposure and unexpected-behaviour risks before release.

What I changed

I translated security and privacy principles into practical development standards and built an automated testing system that behaves like an attacker, creates difficult test cases and reports where safeguards fail.

The result

Developers gained a repeatable way to test AI systems earlier, compare findings and address weaknesses before production.

What it taught me

AI security works best when it shapes the development process from the beginning, rather than appearing as a final approval step.

Case 02 · Private AI

Exploring a more controllable model strategy

Deployed and evaluated an open-source language model inside a controlled enterprise cloud environment.

The problem

Organisations want the benefits of generative AI, but sensitive use cases raise important questions about data control, operating cost and reliance on external services.

What I changed

I deployed and tested an open-source model within an enterprise-controlled environment, assessed its operational fit and turned the findings into practical guidance for engineering teams.

The result

Decision-makers gained practical evidence about where private models may offer greater control and where managed services may still be the better choice.

What it taught me

Model choice is not a popularity contest. The right answer depends on privacy, performance, cost, support and how much operational responsibility a team can own.

Case 03 · Internal capability

Turning a strategic AI system into sustainable ownership

Took end-to-end technical ownership of an important enterprise AI system and helped strengthen the organisation's internal capability around it.

The problem

A complex AI initiative needed stronger internal understanding and clearer ownership so future development would not remain dependent on outside specialists.

What I changed

I took responsibility across core engineering, data analysis, testing, technical planning and delivery, while translating the work into clear tasks and guidance for the wider team.

The result

The team gained clearer internal ownership, stronger technical continuity and a better foundation for future improvements.

What it taught me

A successful AI system should leave an organisation more capable—not permanently dependent on the people who built its first version.

Career path

The perspective behind the work.

My career moved from enterprise software to analytics, data leadership and applied AI. The consistent pattern is to understand the problem, build responsibly and leave the organisation more capable than before.

2025 — Present

Senior AI Engineer · Rentokil Initial · Group AI Team

Shaping secure, reusable AI foundations and helping technical teams build with confidence.

2024 — 2025

Business Insight Manager · Rentokil Initial

Used AI, forecasting and better data to reduce manual work, improve planning and lower costs.

2022 — 2024

Data Analyst · Checkatrade

Used customer data to improve retention, marketing decisions and revenue forecasts.

2014 — 2020

Technology Analyst · Infosys

Led software teams from the first client conversation through development, launch and support.

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