Lead AI Engineer

Who you are

• A leader who earns authority through technical credibility rather than title
• Still an engineer at heart — close enough to the code and the designs to have an opinion worth defending
• Comfortable being accountable for outcomes you did not personally build
• Someone who has held a technical position against pushback, revised it when the pushback was right, and can tell the difference
• Direct with stakeholders and with your team — you surface problems early, including the ones that reflect badly on your own decisions
• Decisive under ambiguity: you make the call on incomplete information and revisit it when better information arrives
• Clear in writing — able to make a technical argument that a non-engineer can follow and act on
• Product-minded and focused on real user outcomes, not just model outputs
• Curious, low-ego, and biased toward action; motivated by what the team ships, not only by what you ship yourself
• Proven experience leading a team of engineers, formally or as a tech lead, with responsibility for what the team delivered
• Track record of owning the technical direction of a system used by teams other than your own
• Experience changing a product or business decision through technical argument rather than escalation
• Experience working directly with non-technical stakeholders on commitments and tradeoffs, not through a manager as intermediary
• Experience growing other engineers through review, mentoring, or design guidance
• Strong ownership mindset and the ability to create clarity in genuine ambiguity
• Proven experience building and deploying AI systems in production
• Strong Python and software engineering fundamentals
• Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning
• Experience integrating AI systems into backend or product workflows
• Ability to design meaningful evaluation, monitoring, and continuous improvement loops
• Experience with cloud infrastructure and containerized deployments
• Strong grasp of the fast-moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisions
• Fluent English (C1)
• Experience in fintech, financial services, risk, compliance, or operations-heavy environments
• Experience with applied ML beyond LLMs, such as classification, anomaly detection, ranking, or document intelligence
• Experience with vector databases, knowledge systems, and retrieval infrastructure
• Experience with model benchmarking, experimentation frameworks, and cost or latency optimization at scale
• Background in startups or as a founder
• Contributions to open-source or visible side projects in AI

What the job involves

• We are looking for a Lead AI Engineer to own the technical direction of AI systems across Finom and lead the engineers who build them
• This is a leadership role from day one
• You will set the technical and quality bar, lead a team of AI engineers, and stay hands-on in the code
• You have done this before — we are not looking for someone stepping into technical leadership for the first time
• You will build AI services for Finom customers, and internal platform solutions that allow other teams to ship AI capabilities of their own
• You take architecture responsibility for the services you and your team develop, along with the alignment that comes with it — translating between business intent and technical reality, and challenging product decisions when they are wrong
• This is not a research role
• It is a hands-on engineering leadership role focused on production-grade AI capabilities that create clear value for customers and the business
• Lead a team of AI engineers — set direction, review designs, and grow their technical scope through review, pairing, and design guidance
• Own the architecture for the services you and your team develop — and write down the decisions, tradeoffs, and rejected alternatives so they can be reviewed and challenged
• Build and ship AI-powered customer and internal solutions using LLMs, RAG, tool calling, workflows, and agentic patterns
• Own AI systems end to end — problem framing, implementation, evaluation, deployment, monitoring, and iteration
• Develop scalable and reliable inference pipelines with strong attention to latency, cost, security, and observability
• Set the evaluation and quality bar — offline evals, online signals, failure analysis, and continuous improvement loops that other teams adopt rather than renegotiate per project
• Design AI systems with auditability, model governance, and EU AI Act obligations built in from the start rather than retrofitted
• Drive AI platform and tooling decisions that improve reuse, speed, and consistency across teams
• Partner with solution managers, domain teams, and engineers to integrate AI into real workflows rather than isolated demos
• Tell a product stakeholder when the thing they asked for is the wrong solution, and be persuasive about the right one
• Negotiate scope and sequencing with domain teams that have their own roadmaps and no obligation to yours
• Decide what to stop doing — deprecate, simplify, or kill approaches that the evidence no longer supports
• Shape the roadmap rather than only execute tickets
• ---Tech Stack---
• Languages: Python, SQL, noSQL, .NET (optional)
• LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw
• Patterns: RAG, tool calling, agent workflows, eval pipelines
• Infrastructure: Docker, Kubernetes, AWS / GCP / Azure
• Data / Platform: Vector databases, event-driven systems, APIs, observability tooling
• ---In your first 3 months---
• You have enough context on the team and the business domains it serves to know which problems are worth solving
• You have taken architectural ownership of your team's services, including the decisions you inherited
• The team's priorities are clear and defensible, and you are who product and business stakeholders come to for them
• You have identified the biggest constraint on the team's delivery — technical debt, unclear ownership, missing skills, or process — and started removing it
• ---By 6 to 12 months---
• Your team has delivered significant AI capabilities to Finom customers, with measurable impact through revenue uplift, cost savings, productivity gains, or risk reduction
• The team delivers predictably: commitments you make on its behalf hold, and when they will not, stakeholders hear it from you early
• The architecture and standards you set are documented, adopted, and used by teams beyond your own
• Quality is systemic rather than heroic — failures are caught by evals and monitoring rather than by customers, and the team debugs its own production issues
• Engineers on your team have grown measurably in scope, and several can lead significant workstreams without you in them
• You shape the AI roadmap together with product and business leadership rather than receiving it, and your "not this way" or "not yet" carries weight