Requirements
- •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
What You'll Do
- •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
Nice to Have
- •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
Benefits
- •Make a genuine impact on the product
- •Work in the EU with flexibility of traveling and working remotely or in a hybrid model across Europe
- •Become a stock options holder through Stock Options Program
- •Receive unwavering support and care in a modern, friendly, and eco-conscious corporate culture
- •Work & Swim program: spend one month in a corporate apartment in Cyprus for work-life balance
About Finom
Finom provides an online payment solution that integrates business accounts, invoicing, and accounting.
Amsterdam, Netherlands
500 - 1000
Finance