We’re looking for a scrappy, experienced AI engineer based in London to join our fintech startup at the ground floor. This role is a good fit for those who are excited about independence, impact, and contributing to the battle against scams and financial crime. If this sounds like a match for you, read on to learn more ⬇️
About us 👋
Nicky (co-founder) previously co-founded Nova Credit, helping immigrants access credit. We’ve served millions of immigrants, raised over $130M (GC, Index, KPCB), built a team of 100+ people across three continents. Before that it was Stanford MBA, Bain, international development, and Cambridge. 🇬🇷-🇬🇧 married to🇰🇷-🇺🇲, parent, intermittent reader/runner, smack talker.
Nico (co-founder) spent the last 4 years as CEO of Casai, LatAm’s largest short-term rental operator with almost 2k apartments in 🇲🇽 and 🇧🇷 and hosted >100k guests. Previously met Nicky as Nova Credit’s first biz hire right after the seed round. Started career @ BCG after studying at Yale 🐶 & Oxford. Born in 🇵🇭 & grew up in Memphis, TN, the birthplace of rock & roll 🎸.
Cyrus (Head of Engineering) has experience across a wide range of industries and has built large-scale systems in a variety of sensitive contexts from banking 🏦 to defence 🛡️. Has been working with Nicky and Nico since 2023. Ex-Palantir and Morgan Stanley, before that Imperial College. Statistically most likely to be found walking my dog 🐕 and climbing rocks 🧗♂️.
And here is a lil video about us as humans (feel free to watch @2x 🙈) 👇.
About you 🤓
Here are some thoughts on who would be successful in the role. We know we won’t be able to assess these from your CV, but our interview process will be designed around them:
Scrappiness: You autonomously drive value without the comforts of robust data infrastructure or huge training datasets. You navigate ambiguity to uncover insights through curiosity and with a bias to action.Breadth: You are unafraid of jumping into new terrain; you have a propensity for picking up new tools and technologies.Quality obsession: You understand how to build production-grade solutions and analytical workflows, and are comfortable applying that rigour at early stage.Problem-solving: You love ambiguous analytical problems, understand how to navigate data quality issues, and anticipate and effectively root cause failures. You have an eye for detail, can see around corners, and understand that prevention is the best cure.Impact: This isn’t a role for a pure researcher — we’re looking for someone who wants to ship real impact, not just papers or dashboards.Some hard skills we’d value
LLM & Gen-AI expertise: Hands-on experience designing and iterating on prompts, building RAG or agentic systems, and working with popular LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face). Comfortable benchmarking and applying evaluation techniques (synthetic or human-in-the-loop) in production.Architecture & MLOps: Proven record automating the ML lifecycle — model / prompt / data pipelines (batch, streaming or real-time), and integrating them seamlessly into production systems.Cloud & DevOps: Comfortable with cloud platforms (AWS or GCP), containerisation (Docker), infrastructure-as-code, and cloud-native ML services (Vertex AI, SageMaker, Bedrock, etc.).Governance & responsible AI: Demonstrated ability to work with sensitive data—good instincts for privacy (PII handling, differential privacy where needed), fairness / bias mitigation, and relevant regulation (e.g. GDPR, UK FCA guidance).Years of experience: Roughly 7+ years; The exact number doesn’t matter, but we’re looking for professionals with a track-record of structuring their own work and thinking strategically while getting their hands dirty.Stage experience: (Perhaps even more important than years of experience!) We value people who have worked in early stage companies and demonstrate fluency with ongoing speed: precision tradeoffs and fast iteration.Industry context: We’re building on top of technical payments infrastructure so prior experience in fintech or banking is a plus, but experience building large-scale systems in any sensitive data context will get you off on the right foot.Not got 100% of qualifications? Research shows that women and under-represented minorities are less likely to apply for a role if they do not have 100% of the requirements. We’re paying attention to this, so please feel free to apply regardless (<2mins) or send us a note on info@tunicpay.com if you’d like to discuss if there’s a fit.
Stuff you’d do
Agent & RAG workflow orchestration – Design version-controlled prompt pipelines, guardrail frameworks, and tool-invocation policies; build tracing and observability as first-class product features; and curate vector-store database.ML/LLM CI-CD & serving infrastructure – Architect and deliver a holistic ML deployment product—from automated retraining through containerised builds to one-click rollouts. Design low-latency, cost-efficient inference endpoints and establish a metrics-driven roadmap that balances performance gains.Lifecycle refinement & data ownership – Define and own the data-product lifecycle: craft high-quality pipelines and govern schema evolution. Provide clear upgrade paths so models and features stay reliable as customer behaviour and business priorities shift.Production monitoring & governance – Productise comprehensive telemetry—latency, accuracy, drift, jailbreak simulations—and embed alerting, privacy, and fairness controls directly into the platform. Translate compliance requirements into actionable product capabilities that scale with growth.Last but not least, the interview process ⏳
We are seeking to make our interview as experiential as possible in order for both sides to get the most signal on what it would be like to work together.
Initial application (<2 mins): Submit your LinkedIn (or CV) in the application link below (we don’t believe in cover letters or unnecessary work here!) 👇Intro call (30 mins): Get to know each other a little and learn more about who we are and what we’re building.Technical screen (60 mins): Discuss past Data/ML projects and do some collaborative problem-solving with one of our engineers.On-site (3h, can be done remotely if needed): Deep dive interviews focused on architecting AI systems, mapping ML solutions to user value, and product thinking. Plus time with the co-founders — this is as much for you as it is for us!References and offer: Ideally over dinner!
