AI Deployment Engineer

BSc Computer Science finalist in London, graduating July 2026 and on track for a First. I build AI systems that move from messy user workflow to deployed runtime, evals, readiness gates, and real iteration.

Forward Deployed Engineering Applied AI LLM Evals Docker / FastAPI / TypeScript
Open CV Hosted Gateway Gateway Source
Scroll for proof, case studies, architecture, and application assets.

Evidence At A Glance

The story is not years of employment. It is dense founder/operator reps in AI workflow design, deployment, evals, and runtime debugging.

95 real teacher messages reviewed across TeachClaw pilot usage.
100/100 teacher-style turns completed in a long-session routing comparison.
35 automated tests passed in the Story Trials full-stack project.
9 FastAPI gateway tests covering auth, evals, readiness, metrics, and audit events.

Why This Fits

I am earlier-career by title, but the work shape is already close to AI deployment: user workflow, LLM system, runtime, evals, launch proof, and iteration.

Positioning

I am not claiming ten years of enterprise delivery. I am claiming unusually direct founder/operator reps in the exact loop FDE and AI deployment teams care about: understand the workflow, build the AI path, ship it, debug it, and turn failures into evidence.

Deployment Loop

Workflow discovery Teacher and field-service workflows converted into concrete AI surfaces.
Production-shaped engineering FastAPI, Docker, CI, health checks, smoke checks, readiness gates, and hosted proof.
Eval-led iteration Scenario packs, source checks, memory boundaries, artifact checks, and promotion summaries.

Case Studies

These are public-safe writeups. They avoid private teacher data, secrets, internal IPs, and TeachClaw source code.

TeachClaw Deployment

Chat-native AI assistant for UK teachers with document generation, marking support, teacher memory, VPS deployment, and eval loops.

Deployment proof

OpenClaw Operator / Evals

Operator cockpit with gateway sessions, plugins, LCM memory, local validation lanes, promotion summaries, and runtime drift discipline.

Operator proof

Story Trials

Full-stack synthetic health data licensing prototype using Next.js, Express, Prisma/PostgreSQL, IPFS, and Story Protocol testnet flows.

Full-stack proof

SparkAssist

Telegram-based electrician assistant for notes, voice input, structured extraction, certificates, reports, quotes, invoices, and RAMS.

Domain-transfer proof

AI Deployment Gateway

Public FastAPI proof with retrieval, eval runs, SQLite persistence, API-key write protection, audit events, readiness gates, Docker, and CI.

Public code proof

Architecture Story

TeachClaw maps cleanly to AI deployment work: user workflow surfaces, a platform layer, an agent/runtime layer, deterministic builders, and a validation loop before live promotion.

Workflow first Teachers ask in chat. The system routes intent into concrete classroom artifacts.
Contracts over vibes LLM output becomes schema-shaped input for deterministic document builders.
Eval before promotion Routing, memory, artifacts, and delivery have local proof lanes before live smoke.
Runtime truth matters Source, gateway-loaded code, runtime mirrors, and live VPS payloads can drift.
TeachClaw and OpenClaw deployment architecture diagram

Recruiter Assets

Fast links for applications, profile cleanup, and technical proof.

CV PDF One-page AI Deployment / FDE positioning with updated Gateway proof. Hosted Gateway API Live FastAPI demo behind Cloudflare Tunnel with health, retrieval, eval, readiness, metrics, and audit routes. Gateway Source FastAPI deployment-readiness proof with retrieval, evals, SQLite, audit events, Docker, and CI. Architecture Diagram Public-safe TeachClaw/OpenClaw deployment architecture diagram.