Senior Software Engineer with 5+ years building and operating production backend, cloud, and AI systems — applied ML since 2021, production LLM agents today. Currently at NatWest, previously Technical Lead at Forwood Safety and full-stack engineer at Launchpad Fintech. I work day to day in Python, AWS, Terraform, Docker, and GitLab CI/CD, and I'm certified on HashiCorp Terraform, Claude, and AWS.
At NatWest I own the infrastructure behind our AI agent platform: MongoDB Atlas and Aurora PostgreSQL provisioned through reusable Terraform modules, services running on AWS ECS, releases moving through GitLab CI/CD. I've co-designed banking agents on OpenAI and Gemini that run in production, and cut roughly $10,000 a year from our AWS bill by fixing how those services scale.
I also built the team's accessibility tooling — a frontend code scanner and a GitLab CI/CD template that drops into any pipeline in three lines. Chosen as the strongest of three solutions built across the business, it now runs in 12 feature teams and has caught around 4,000 accessibility issues before they reached production. It's the work I'm proudest of: it makes the right thing the easy thing for every team that adopts it.
I've moved from full-stack developer to release manager to Technical Lead, so I've owned more than code — release cycles, mentoring, and architecture calls alongside product, mobile, and frontend teams. The part I enjoy most is the unglamorous part: making a system observable, predictable, and cheap to run. I've taken services from a whiteboard sketch to production, and I've been the person on call when they broke.
The work I want more of is exactly this — AI systems held to real engineering standards: observable, cost-aware, and boring to operate.
- ~4,000
- Accessibility issues caught before production, across 12 teams
- ~$10k
- Annual AWS spend removed via ECS auto-scaling
- 23
- Languages served by the localization platform I built