Senior AI Engineer

• We are looking for a Senior AI Engineer to design, build, and operate AI systems that solve real business problems across Finom

• You will work on high-impact initiatives across onboarding, customer support, AI accounting, fraud and risk workflows, document understanding, internal automation, and agentic systems used by multiple teams

• This is not a pure research role. It is a hands‑on engineering role focused on delivering production‑grade AI capabilities that create clear value for customers and the business

• Build and ship AI‑powered product and internal solutions using LLMs, RAG, tool calling, workflows, and agentic patterns

• Own AI systems end‑to‑end: problem framing, architecture, implementation, evaluation, deployment, monitoring, and iteration

• Partner closely with solution managers, domain teams, and engineers to integrate AI into real workflows rather than isolated demos

• Design quality and evaluation frameworks for AI systems, including offline evals, online signals, failure analysis, and continuous improvement loops

• Develop scalable and reliable inference pipelines with strong attention to latency, cost, security, and observability

• Work on use cases such as onboarding, customer care, transaction and document classification, knowledge assistants, fraud detection, and operational automation

• Contribute to AI platform and tooling decisions that improve reuse, speed, and consistency across teams

• Challenge assumptions, propose better approaches, and help shape the roadmap rather than only execute tickets

• Experiment boldly, learn quickly from failures, and turn insights into stronger systems and better practices

• What Success Looks Like:

• In your first 6 to 12 months, you will:

• Become fully embedded in the team and business domains you support

• Deliver at least one significant AI capability into production

• Generate visible impact through revenue uplift, cost savings, productivity gains, or risk reduction

• Raise the technical bar for how Finom builds, evaluates, and operates AI systems

• Help other teams adopt AI more effectively through strong engineering practice and pragmatic guidance

• You do not need experience with every item, but this role will likely involve technologies such as:

• 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

This role is for someone who can move comfortably from prototype to production: shaping the solution, building the system, measuring quality, and improving it over timeStrong Python and software engineering fundamentalsStrong at turning ambiguous business problems into robust technical solutionsHands‑on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine‑tuningAbility to design meaningful evaluation, monitoring, and continuous improvement loopsCurious, proactive, low‑ego, and biased toward actionAutonomous, pragmatic, and able to keep momentum without heavy supervisionProduct‑minded and focused on real user outcomes, not just model outputsStrong grasp of the fast‑moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisionsStrong ownership mindset

and ability to work through ambiguityProven experience building and deploying AI systems in productionExperience with cloud infrastructure and containerized deploymentsComfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production supportActively experiments with new AI models, tools, and agentic patterns, and can evaluate which approaches are worth productionizingClear in communication and comfortable working across functionsSomeone who actively keeps up with the fast‑moving AI landscape and can separate hype from what is actually usefulExperience integrating AI systems into backend or product workflowsFluent EnglishA strong software engineer with deep Python experience and a track record of shipping production systemsExperience in fintech, financial services, risk, compliance, or operations‑heavy environmentsExperience with applied

ML beyond LLMs, such as classification, anomaly detection, ranking, or document intelligenceExperience with vector databases, knowledge systems, and retrieval infrastructureExperience with model benchmarking, experimentation frameworks, and cost or latency optimization at scaleBackground in startups or as a founderContributions to open‑source or visible side projects in AI