Principal Software Engineer helping teams build reliable AI-powered software.
I help SaaS companies and engineering teams design better systems, adopt AI responsibly, improve developer productivity, and make high-quality technical decisions.
About
I work at the intersection of software architecture, product engineering, AI adoption, and engineering leadership.
Over the course of my career I have helped companies design systems that scale, adopt AI tools responsibly, build strong engineering cultures, and move from vague technical challenges to clear decisions and executable plans.
I care about pragmatic engineering — solutions that work in practice, not just on whiteboards. I value maintainability, developer experience, and business outcomes equally.
I can go deep technically when needed, and communicate clearly with founders, executives, and cross-functional teams.
Pragmatic by nature
I favour solutions that are correct, maintainable, and proportional to the problem — not the most sophisticated approach available.
Business-oriented thinking
Good engineering decisions are also good business decisions. I keep outcomes and trade-offs in view at every layer.
Clear communication
I translate technical complexity into clear language for founders, investors, and leadership — and back again for engineering teams.
Services
Five focused areas where I provide senior technical expertise.
AI Engineering Advisory
I help teams identify practical AI use cases, evaluate model and tooling options, design AI workflows, and move from prototype to production.
- AI opportunity audits
- LLM application architecture
- RAG system design
- Internal AI tool development
- AI workflow automation
- Model and vendor selection
- Risk, security, and data governance
- Production-readiness reviews
Software Architecture Review
I review architecture, codebases, infrastructure, and engineering practices to identify risks, simplify complexity, and create a practical improvement roadmap.
- System design review
- Scalability assessment
- Technical debt prioritisation
- Maintainability review
- Cloud cost review
- CI/CD and testing review
- Reliability and observability
- Architecture decision records
Fractional Principal Engineer / CTO Advisory
I provide senior technical leadership for companies that need experienced guidance without hiring a full-time executive or principal engineer.
- Technical strategy
- Architecture direction
- Engineering roadmap support
- Hiring and interview support
- Team mentoring
- Vendor and tooling decisions
- Executive technical communication
- Engineering process improvements
Engineering Productivity & AI Enablement
I help engineering teams use AI tools effectively while protecting quality, security, and long-term maintainability.
- AI coding assistant workflows
- Developer productivity improvements
- Code review practices
- Testing and refactoring with AI
- Team AI usage guidelines
- Secure AI adoption
- Engineering workflow automation
- Internal training and workshops
Technical Due Diligence
I help founders, investors, and leadership teams understand the true state of a technology product, platform, or engineering organisation.
- Architecture risk review
- AI claims validation
- Scalability assessment
- Security and data risk review
- Engineering team assessment
- Roadmap realism check
- Build-vs-buy evaluation
- Investment and acquisition support
How I Work
A clear, collaborative process from first conversation to lasting improvement.
Discover
I start by understanding your context — the business goals, the technical landscape, the team, and the specific challenges you are facing.
Assess
I review your systems, architecture, code, processes, and decisions to identify the real issues — not just the visible symptoms.
Design
I produce clear recommendations, architecture proposals, or decision frameworks tailored to your situation and constraints.
Implement or Guide
Depending on the engagement, I either build alongside your team or guide your engineers through implementation with ongoing support.
Review & Improve
I review outcomes, refine the approach, and help establish processes that sustain improvement beyond the engagement.
Engagement Models
Flexible ways to work together based on your situation and goals.
AI / Architecture Audit
A focused assessment of your current systems, AI opportunities, risks, and next steps — with a clear, actionable output.
- Technical assessment report
- Prioritised recommendations
- Architecture diagrams where needed
- 90-day roadmap
- Executive summary
- Team walkthrough session
Advisory Retainer
Ongoing senior technical guidance for founders, CTOs, or engineering leaders who need experienced input without a full-time hire.
- Weekly or biweekly advisory calls
- Architecture and code reviews
- Decision support
- Hiring and interview guidance
- Async guidance via chat
- Roadmap reviews
Workshop or Training
Focused sessions for engineering teams on high-value topics that improve decision-making, quality, and velocity.
- AI for engineering teams
- Production-ready LLM applications
- Architecture decision-making
- Engineering productivity
- AI-assisted development workflows
Prototype / Proof of Concept
A short engagement to validate a technical idea — with a working prototype, honest assessment, and a clear path forward.
- Working prototype
- Technical feasibility assessment
- Recommended architecture
- Cost and risk estimate
- Next-step roadmap
Problems I Can Help With
If any of these sound familiar, let's talk.
We want to use AI but do not know where to start.
Our prototype works, but we do not know how to productionise it.
Our architecture is slowing us down.
We need senior technical judgment but are not ready to hire full-time.
Our engineering team is using AI tools inconsistently.
We need to reduce technical debt without stopping product delivery.
We need an independent review of our platform.
We are evaluating an AI startup or technical acquisition.
We need better engineering processes and decision-making.
Case Studies
Real-world examples of technical challenges and how they were approached.
These case studies are illustrative templates. Details will be updated with real project information.
Background & Expertise
What I bring to every engagement.
Principal-level engineering experience
Deep technical seniority across the full software development lifecycle — from architecture design to production operations.
SaaS and production systems background
Hands-on experience designing and operating production systems at SaaS companies — including the reliability, scalability, and operational challenges that come with it.
Architecture and system design expertise
Strong grounding in distributed systems, API design, data modelling, and the trade-offs that define long-lived systems.
Cross-functional communication
Comfortable working across product, engineering, and business contexts — translating technical decisions into business terms and back.
Practical AI adoption experience
Real-world experience helping teams adopt AI thoughtfully — not just prototyping, but building production-ready AI systems that hold up under real conditions.
Engineering leadership perspective
Experience operating at the principal and leadership level — mentoring engineers, guiding teams through technical decisions, and shaping engineering culture.
Insights
Thinking on AI engineering, software architecture, and technical leadership.
Full articles coming soon.How engineering teams should adopt AI without creating chaos
AI tools offer real productivity gains — but only with deliberate practice. This is what separates teams that benefit from teams that regress.
From AI prototype to production system
Most AI prototypes work in demos. Making them reliable in production is a different engineering problem — and it starts before you write the first line of code.
What founders should know before building with LLMs
LLMs are powerful and unreliable at the same time. Understanding the failure modes before you commit to a product direction saves months of rework.
Architecture reviews: what good teams inspect regularly
The best engineering teams treat architecture as a living thing — regularly inspecting assumptions, dependencies, and risks before they become problems.
The future role of principal engineers in AI-native companies
AI is changing what senior engineers do, not eliminating the need for them. The principal engineer role is evolving — here is how.
How to evaluate technical debt pragmatically
Not all technical debt is equal, and not all of it needs to be paid down. A pragmatic framework for deciding what to address and when.
AI coding tools: productivity boost or quality risk?
The answer depends entirely on how your team uses them. Here is a framework for getting the benefit without the risk.