Senior AI Engineer (Remote)

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

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. 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.

What You Will Be Doing

• 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

Who You Are

• A strong software engineer with deep Python experience and a track record of shipping production systems
• Comfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support
• Strong at turning ambiguous business problems into robust technical solutions
• Product-minded and focused on real user outcomes, not just model outputs
• Autonomous, pragmatic, and able to keep momentum without heavy supervision
• Clear in communication and comfortable working across functions
• Curious, proactive, low-ego, and biased toward action
• Someone who actively keeps up with the fast-moving AI landscape and can separate hype from what is actually useful

Must-Haves

• 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 ownership mindset and ability to work through ambiguity
• Actively experiments with new AI models, tools, and agentic patterns, and can evaluate which approaches are worth productionizing
• Strong grasp of the fast-moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisions
• Fluent English

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

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