Website:
unloq.co
Job details:
Build at the frontier of AI 🧠
Even the best-run companies struggle for answers they can trust. Unloq fixes that. Period. Answers in seconds, in plain language, with the evidence behind every number. Live with enterprise customers, we're now building towards prescriptive intelligence: not just what happened, but what to do next.
Come build the next big AI platform with us!
We're looking for a one of a kind Full Stack AI Engineer: someone who can take a client problem nobody has solved before, a messy data environment, a workflow held together by spreadsheets and goodwill, and ship a production AI system that people actually rely on. You'll work directly with the founders and the engineering team, owning client delivery end-to-end.
The way software gets built changed. We build like it. This is agentic engineering, not ticket-taking. Anyone can prompt a model. We want someone who directs Claude Code across parallel workstreams, reads every line it produces, and knows exactly when the machine is wrong. You'll ship in days what used to take teams a quarter, and you'll be the deepest engineer in the room when it counts.
🔑 The Role
You'll own the journey from client requirement to production system.
- Designing, building and deploying LLM-powered conversational systems: chatbots, workflow assistants and domain-specific AI tools (including patient-facing systems), tailored to each client's data and domain
- Full-stack ownership: backend (FastAPI/Python), frontend integration, LLM orchestration and production deployment. If it ships to a client, it's yours
- Engineering the LLM layer properly: inference pipelines, context management, evals, caching and cost-efficient model routing. Prompt engineering is table stakes; we expect pipeline thinking
- Connecting AI to the real world: client APIs, webhooks, MCP servers and event-driven automations that remove manual work rather than add to it
- Production-grade by default: Docker, CI/CD, monitoring, drift detection, testing and documentation on every engagement. Demos are easy; reliability is the product
- Sitting in client rooms: translating requirements into technical specs, shipping weekly demos, escalating scope creep early, and feeding what you learn back into the platform
⚙️ How We Work
- All code is reviewed before it merges. Architectural decisions sit with the Principal AI / Systems Architect; implementation decisions are yours
- Client delivery code lives in client-specific repositories, separate from the core platform
- Weekly standup with the founding team, daily async updates in Slack
- Claude Code, Cursor and Copilot aren't tolerated here, they're expected. If you're not building agentically, you're building slowly
📦 What You'll Have Built
In your first six months:
- Two or more production AI systems live with enterprise clients, from first discovery call to deployment
- A reusable delivery stack: orchestration patterns, eval harnesses and deployment templates so no engagement starts from a blank repo
- A monitoring and validation layer that catches drift and quality issues before the client does
- Documentation and handover packs that make every deployment maintainable without you
- A weekly shipping cadence clients set their watch by
🧩 Who This Is For
- A strong T1 technical degree, with an Master's in an AI native program preferred. We hire on evidence, not just pedigree
- Serious Python and full-stack fundamentals. Non-negotiable, and how we'll assess you. You'll be handed a real problem and a hard time limit
- Native to agentic development: Claude Code or similar as your primary way of building, with the depth to review, correct and harden everything it generates
- Production LLM experience: you've shipped something real on model APIs and you understand context management, token economics, latency and failure modes
- DevOps competence: Docker, CI/CD, cloud deployment (AWS preferred)
- Client-ready communication. You can explain a technical trade-off to a non-technical stakeholder without dumbing it down or drowning them
- You own delivery and keep momentum without being chased
- Nice to have: RAG and retrieval systems, MCP, eval frameworks, event-driven architecture, exposure to healthcare or other regulated industries
📍Role Basics
- Role: Full Stack AI Engineer (Full-time)
- Location: Fully Remote, Start ASAP
- Compensation: $250pm with a world-class mentorship program and the real prospect of a FT role
Why Join Unloq®?
- Learn from the best. You'll work directly with the founders on live enterprise deployments, and grow faster than any grad scheme would allow
- Real craft, real ownership. Your code ships to paying enterprise clients. There is no layer between your work and the outcome it drives
- Direct founder access. Small team, big problems, zero bureaucracy
- Modern stack. Claude and Claude Code at the core, Databricks underneath, ClickUp for delivery
- We're building something that doesn't exist yet: the layer that makes AI trustworthy enough for the boardroom, not just the back office. It's early, it's hard, and the right people find that exciting. If you take pride in flawless execution done properly, this is your role.
Unloq is an equal opportunities employer. We hire on talent and potential, and we welcome applicants from every background and route into the industry. If you meet most of this and not all of it, apply anyway.
Click on Apply to know more.
