Responsibilities
- Develop and deploy AI-driven product and internal solutions using LLMs, RAG, tool calling, workflows, and agentic patterns.
- Take full ownership of AI systems from problem framing through architecture, implementation, evaluation, deployment, monitoring, and iteration.
- Collaborate closely with solution managers, domain teams, and engineers to embed AI into practical workflows rather than isolated demonstrations.
- Create quality and evaluation frameworks for AI systems, including offline evaluations, online signals, failure analysis, and continuous improvement cycles.
- Build scalable and reliable inference pipelines with a focus on latency, cost, security, and observability.
- Address use cases such as onboarding, customer care, transaction and document classification, knowledge assistants, fraud detection, and operational automation.
- Contribute to decisions on AI platform and tooling to enhance reuse, speed, and consistency across teams.
- Challenge assumptions, propose improved approaches, and help shape the roadmap rather than merely executing tasks.
- Experiment boldly, learn quickly from failures, and translate 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: Embark on this exciting journey with us and enjoy the flexibility of traveling and working remotely or in a hybrid model across Europe.
- Become a stock options holder: Unlock your inner entrepreneur and align your aspirations with ours through our Stock Options Program. This exciting opportunity is available to every team member, from junior team members to our founders.
- Receive unwavering support and care: Finom stands by you at every step, embodying our commitment to your well-being and success reflected in our modern, friendly, and eco-conscious corporate culture. We offer constant support and care to ensure your Finom experience is successful and fulfilling.
- Work & Swim program: Immerse yourself in our exclusive Work & Swim Program. Spend one month in a comfortable corporate apartment in enchanting Cyprus. It's the ideal opportunity to strike the perfect work-life balance while enjoying breathtaking Mediterranean views.
Work Arrangement
Remote within the EU
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.
Example Tech Stack
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.
Other
- Language: Fluent English required
- Work mode: Remote within the EU
- 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.
