Software Engineer with 5+ years of experience designing and operating backend systems that serve millions of users. Currently at DAZN, where I build the microservices behind purchase, entitlement and access flows for global sports streaming — services measured in availability, latency and revenue impact rather than lines of code.
My core strength is backend and distributed systems engineering: designing service boundaries, modelling data for access patterns rather than convenience, and making cloud-native systems observable and resilient under real production load. I work primarily in Node.js, NestJS, TypeScript and Go, on AWS infrastructure defined end to end in Terraform.
Alongside that, I build AI-powered product software. AutoClipr — an AI video clipping SaaS — is my own product, covering everything from transcription and LLM-driven segment selection through to media pipelines, billing and multi-platform publishing integrations. My interest in AI is applied and product-shaped: turning models into reliable, cost-controlled features that ship.
Earlier in my career I built blockchain and NFT platforms from scratch, integrating smart contracts, custodial wallets and crypto payment rails — work that gave me an early and lasting appreciation for correctness, idempotency and security in systems that move value.
I approach engineering with a product mindset: understand the user outcome, choose the simplest architecture that survives contact with scale, and own the result through to production.
- Senior / Lead Backend Engineering roles
- Distributed systems and cloud architecture work
- AI product engineering and applied LLM systems
- Technical collaboration on streaming, payments and developer tooling
- Open-source contribution and mentoring
Languages
Frontend
Backend & Databases
Cloud, DevOps & Tooling
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| Domain | Proficiency | Details |
|---|---|---|
| Applied LLM Engineering | Production | Prompt design, structured output, tool use and cost/latency control in live product features |
| Speech & Transcription | Production | Audio extraction, transcription pipelines and word-level timing for automated video segmentation |
| AI Product Architecture | Production | Queue-backed inference workloads, retries and idempotency, graceful degradation under model failure |
| Media & Video Processing | Production | FFmpeg pipelines, rendering, encoding and multi-format delivery at scale |
| Retrieval & Embeddings | Working | Vector search and semantic retrieval for content understanding |
| ML Fundamentals | Foundational | Model evaluation, prompt/output quality measurement, error analysis |
AutoClipr — AI Video Clipping SaaS
An AI-powered SaaS that turns long-form video into short, publish-ready clips. Handles the full pipeline: ingest, transcription, LLM-driven segment selection, rendering, and direct publishing to social platforms with per-account OAuth and analytics.
| Stack | Next.js · TypeScript · Node.js · PostgreSQL · AWS · FFmpeg · LLM APIs |
| Scale | TODO — add real numbers (users, videos processed, clips generated) |
| Performance | TODO — add real numbers (processing time per video, p95 latency) |
| Security | OAuth 2.0 integrations, scoped platform tokens, encrypted credential storage |
| Impact | TODO — add real outcome (paying users, retention, revenue) |
| Repository | autoclipr.com · private source |
Built and operated solo, end to end. The engineering interest is less in the model calls and more in everything around them: making a long-running, failure-prone, GPU-and-bandwidth-expensive pipeline behave like a predictable product — idempotent job handling, partial-failure recovery, cost ceilings per tenant, and platform API integrations that change under you without warning.
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Backend engineer on the systems powering purchase, entitlement and access for a global sports streaming platform serving millions of subscribers across many markets.
Scope of work
- Designed and enhanced backend services enabling seamless purchase and access across global streaming products
- Built and operated scalable microservices in Node.js, NestJS and TypeScript on AWS
- Developed and optimised APIs serving millions of users across sports streaming platforms
- Implemented secure payment integrations covering subscription and recurring billing workflows
- Delivered cloud-native architecture on API Gateway, Lambda, S3, DynamoDB and CloudWatch, provisioned with Terraform
- Partnered across multiple teams to ship highly available, resilient services under production SLAs
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Built blockchain and NFT platforms from the ground up, spanning smart contract integration, custodial wallet infrastructure and crypto payment rails.
Scope of work
- Built blockchain and NFT platforms from scratch, from data model through to production deployment
- Integrated smart contracts, Fireblocks custody, MetaMask and Circle Payments
- Improved system performance and reliability through extensive testing and optimisation
- Managed cloud infrastructure, monitoring and deployment pipelines
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learning:
- Distributed systems design and consensus fundamentals
- Go concurrency patterns and high-throughput service design
- Applied LLM evaluation and cost-efficient inference
building:
- AutoClipr — AI video clipping SaaS, end to end
- Event-driven backend services on AWS
- Infrastructure as Code with Terraform
exploring:
- Streaming and low-latency media delivery architecture
- Vector search and retrieval-augmented systems
- Platform engineering and developer experience
open_to:
- Senior and Lead backend engineering roles
- Distributed systems and cloud architecture work
- AI product engineering collaboration
- Open-source contribution and mentoring
Good architecture is the set of decisions you do not have to revisit under load.