Lead AI Engineer (Remote in Europe)

Openkrill; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

"Perks"

Job summary

Finom is seeking a Lead AI Engineer to own the technical direction of AI systems across Finom and lead the engineers who build them. This is a hands-on leadership role from day one, combining architecture responsibility with active coding and platform decisions.

You will build AI services for customer and internal platforms, focusing on production-grade capabilities that deliver clear customer and business value. We expect strong Python fundamentals and experience with LLM-based workflows.

Qualifications

• Experience leading engineers with accountability for team delivery.
• Ability to influence product decisions through technical arguments.
• Proven production-grade AI system deployment experience.

Responsibilities

• Lead AI engineering team, set direction and review designs.
• Own architecture for services and document decisions.
• Build AI-powered customer and internal solutions using LLMs and workflows.
• Own end-to-end AI systems including deployment and monitoring.
• Design scalable inference pipelines with latency, cost, security, and observability in mind.

Skills

Team leadership

Technical credibility

Hands-on coding

Python

Communication

Tools

Docker

Kubernetes

LLM tools

PyTorch

Job description

About Finom

Finom is a European tech startup headquartered in Amsterdam, and we’re on a journey towards revolutionizing the financial landscape for entrepreneurs worldwide. Our mission is to develop an all-in-one financial B2B solution that integrates banking functions, accounting, financial management, and invoicing into a seamless, mobile-first platform.

We recently closed a €115 million Series C equity round (around $133 million), bringing our total funding to approximately $346 million. This significant investment follows a $105 million growth funding round from General Catalyst, a long-term backer since 2021 known for supporting companies like Airbnb, HubSpot, KAYAK, and Stripe.

Finom’s platform goes beyond traditional banking, offering invoicing and a growing suite of features, including AI-enabled accounting, aiming to simplify financial management for entrepreneurs. We’re actively expanding our reach across key EU markets like Germany, France, the Netherlands, Italy, and Spain.

At Finom, we’re not just redefining the entrepreneurial experience - we’re empowering our employees to make a real difference. Your work matters, and your impact extends far beyond product metrics. We nurture innovation and an inspiring work environment where bold ideas thrive, prioritizing thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and our business as a whole.

Maintaining our start-up spirit, we prioritize thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and, of course, our business.

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.

What You Will Be Doing

• - 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

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

Must-Haves

• - 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)

Nice-to-Haves

• - 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

Example Tech Stack

• - Languages: Python, SQL, noSQL
• - 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

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

What Success Looks Like

In your first 3 months

• - You have enough context on the team and the business domains it serves to know which probl