Responsibilities
- 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
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
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
- Join our upward trajectory, and grow with us. We provide the resources and opportunities for continuous personal and professional development, empowering you to make a genuine impact on our evolving product
- Work in the EU
- Become a stock options holder
- Receive unwavering support and care
- Work & Swim program
Additional Information
- Equal Opportunity Statement At Finom, we're an equal opportunity employer and value diversity at our company. We embrace diversity and invite applications from all walks of life. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, disability status, or other applicable legally protected characteristics.
