Professional Summary
I am the engineer who gets AI from a working demo into a business that has to live with it. As Principal Forward Deployed Engineer at a Swiss corporate finance advisory firm I run a five-person team and personally own the architecture, integrations and production accountability across four products, reporting weekly to a finance-led board. I build with AI coding agents under a three-gate review and evaluation process, which is how a small team ships integrations with Open Banking, Stripe, Firebase and twelve LLM providers, and I carry the pager when it breaks. Before engineering leadership I spent years on the client side: technical product management at Groupon and enterprise account management for a marketing-technology platform, working with government and multinational accounts across Hong Kong, Singapore, Taiwan and Macau. MSc Computer Science, AI and Data Science (Merit).
Core Capabilities
- Discovery to production, not demos
- SLAs, audit trail and rollback paths
- On-call; escalation point for incidents
- Scoped, dated deliveries for the board
- Open Banking, Stripe, Firebase and Firestore
- Cloudflare Pages, Functions, Workers, D1, R2
- AWS and FIX connectivity
- 12 LLM providers; Groq and Cerebras LPU inference
- Policy gates and evidence packs
- Human-review escalation for high-risk cases
- Per-call cost ledger
- Audit ledger on every AI call
- Thin working prototype plus live dashboard, not slide decks
- Weekly board reporting
- Board, investor and regulatory requirements into scoped deliveries
- Client-side background: account management and technical product management
- Five-person team today
- 8 engineers mentored at Pacific Cloud
- Seniors ship v1, juniors iterate under review
- Architecture reviews and AI/ML working practices
- Claude Code, Codex CLI, Gemini CLI, Cursor
- Eval harness and three review gates
- Evaluation criteria defined before build
- I review, debug and own what the agents produce
Professional Experience
Principal Forward Deployed Engineer
Swiss corporate finance advisory firm building an applied-AI product portfolio: Qorinix (ultra-low-latency AI inference cloud), LaSpend (read-only AI money assistant over UK Open Banking), Fixxmi (Swiss consumer service marketplace) and an AI-native trading desk. I take each product from discovery to production with a five-person team and report weekly to a finance-led board.
- Own end-to-end architecture and production accountability across all four products on AWS, Cloudflare (Pages, Functions, Workers, D1, R2) and Firebase; I am the escalation point for production incidents
- Designed and shipped the Qorinix inference control plane with AI coding agents under a three-gate review and evaluation process: multi-tier model router across 12 providers (OpenAI, Anthropic, Gemini, DeepSeek, Qwen, Groq, Cerebras and others), streaming SSE, per-call cost ledger, usage-based billing and entitlements; targets sub-200ms time-to-first-token
- Integrated LaSpend with regulated UK Open Banking data, Stripe and Cloudflare D1: subscription detection, waste scoring and assisted cancellation, designed read-only by policy with human approval for every user-triggered action and deterministic fallback when models fail
- Delivered Fixxmi's AI lead matching (Next.js 15, Firebase Cloud Functions, Firestore europe-west6) across 12+ service categories in DE-CH/EN within GDPR and nFADP boundaries, with pay-per-lead Stripe credit packs
- Built the governance loop applied to every AI call: policy check, evidence-pack retrieval, prompt registry, model routing, audit ledger and human-review escalation for high-risk cases
- Ran a 12-provider inference benchmark (LLM Arena, live on my portfolio site) to ground model, latency and cost decisions; introduced token tiering and self-hosted models for non-latency-critical workloads to cut API spend
- Translate board, investor and regulatory requirements into scoped, dated deliveries; win decisions with thin working prototypes and live dashboards rather than slide decks
AI Engineer & Technical Architect
Promoted internally from Senior Software Engineer to lead the firm's AI transformation across SaaS, analytics and market-intelligence products.
- Built and operated a real-time ML inference service (feature pipelines, model serving, monitoring) for the firm's analytics products, with latency and accuracy gates defined before each release
- Implemented enterprise retrieval (RAG) and market-intelligence NLP pipelines, including the evaluation methodology used to accept them into production
- Directed the launch of three multi-tenant SaaS platforms; ran architecture reviews and established AI/ML working practices
- Mentored an agile team of 8 engineers: seniors ship v1, juniors iterate under review
- Led quantitative-markets decision support research: cross-asset anomaly detection and price forecasting
Senior Software Engineer
- Built an enterprise document management SaaS with real-time collaboration for corporate clients
- Built e-commerce platform capabilities including multi-currency payments; the SEO programme reached top search rankings
- Pioneered the firm's blockchain product work: smart-contract products, NFT marketplace support and DeFi analytics
Senior Product Manager (Technical) / Web & Content Manager
- Led platform modernisation from a monolithic to a service-oriented architecture as technical product manager, coordinating engineering delivery and business stakeholders
- Introduced an A/B testing and SEO programme that delivered 150% traffic growth
- Ran digital product operations and commercial prioritisation for the marketplace: merchandising, pricing structure and merchant coordination in an Agile environment
Senior Operations Manager
- Oversaw strategic and operational delivery for custom software solutions in the telecoms sector, achieving a 25% annual growth rate
- Led a multi-disciplinary team across the full project lifecycle
Core Technical Competencies
Education
Modules: Deep Machine Learning, Intelligent Agents, Data Science & Mining, Applying AI, Cloud Computing, Research Methods
Selected Deployments
Problem: the board wanted "instant" AI responses for real-time agents, voice and trading alerts, inside a latency budget, provider cost and failover constraints.
Shipped: multi-tier router across 12 providers, streaming SSE, per-call cost ledger, usage-based billing and entitlements.
Outcome: targets sub-200ms TTFT (p50), measured as TTFT p50/p95 per provider in the LLM Arena benchmark; token tiering cut API spend for non-latency-critical work.
Problem: read-only AI money assistant over FCA-regulated Open Banking data; PII minimisation, never moving money.
Shipped: Open Banking API, Stripe, Cloudflare Pages + Functions + D1; human approval for every user-triggered action, deterministic fallback, verified action receipts.
Outcome: live in production; measured by recurring-spend detection precision and cancellation completion rate.
Problem: Swiss consumer marketplace needed AI lead matching across 12+ categories in DE-CH/EN under GDPR and nFADP, with Firestore europe-west6 residency.
Shipped: Next.js 15, Firebase Cloud Functions, Firestore, pay-per-lead Stripe credit packs.
Outcome: live; measured by match acceptance rate.
Problem: every AI call across four products must be policy-checked, costed and auditable, with a human path for high-risk cases.
Shipped: policy check, evidence-pack retrieval, prompt registry, model routing, action runtime, audit ledger, human-review escalation, policy feedback.
Outcome: applied to every AI call; measured by audit completeness and escalation rate.
Problem: a live, inspectable proof of hands-on work: a chatbot that must answer fast, stay inside staged daily budgets and hold a hardened system prompt.
Shipped: Next.js edge runtime on Cloudflare Pages; five-provider cascade (Groq, Cerebras, Gemini, DeepSeek, OpenRouter) with timeouts and fallback; signed-cookie rate limiting.
Outcome: live on my portfolio site; measured by fallback rate and p95 response time.