Responsibilities

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 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

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

Qualifications

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 time A strong software engineer with deep Python experience and a track record of shipping production systems

Hands‑on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine‑tuning

Autonomous, pragmatic, and able to keep momentum without heavy supervision

Clear in communication and comfortable working across functions





Experience with cloud infrastructure and containerized deployments

Fluent English

Ability to design meaningful evaluation, monitoring, and continuous improvement loops

Proven experience building and deploying AI systems in production

Strong grasp of the fast‑moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisions

Someone who actively keeps up with the fast‑moving AI landscape and can separate hype from what is actually useful

Product‑minded and focused on real user outcomes, not just model outputs

Curious, proactive, low‑ego, and biased toward action

Strong at turning ambiguous business problems into robust technical solutions

Actively experiments with new AI models, tools, and agentic patterns, and can evaluate which approaches are worth productionizing

Experience integrating AI systems into backend or product workflows

Strong Python and software engineering fundamentals

Comfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support

Strong ownership mindset and ability to work through ambiguity

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

📌 Senior AI Engineer (Madrid)
🏢 Finom
📍 Madrid