Senior AI Engineer

About the role

Join Finom as a Senior AI Engineer, where you will design, build, and operate AI systems that address real business challenges. This hands-on engineering role focuses on delivering production-grade AI capabilities that create clear value for customers and the business. You will work on high-impact initiatives across various domains, including onboarding, customer support, AI accounting, fraud detection, and internal automation. Key missions: Design, build, and operate AI systems that solve real business problems across Finom, moving from prototype to production.. Own AI systems end-to-end, including problem framing, architecture, implementation, evaluation, deployment, monitoring, and iteration.. Partner closely with solution managers, domain teams, and engineers to integrate AI into real workflows, and contribute to AI platform and tooling decisions. Profile: - Strong at turning ambiguous business problems into robust technical solutions - Fluent English - Must-Haves - Autonomous, pragmatic, and able to keep momentum without heavy supervision - Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning - Actively experiments with new AI models, tools, and agentic patterns, and can evaluate which approaches are worth productionizing - Strong Python and software engineering fundamentals - Product-minded and focused on real user outcomes, not just model outputs - A strong software engineer with deep Python experience and a track record of shipping production systems - Ability to design meaningful evaluation, monitoring, and continuous improvement loops - Proven experience building and deploying AI systems in production - Strong ownership mindset and ability to work through ambiguity - Comfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support - Curious, proactive, low-ego, and biased toward action - Clear in communication and comfortable working across functions - Someone who actively keeps up with the fast-moving AI landscape and can separate hype from what is actually useful - Experience integrating AI systems into backend or product workflows - Experience with cloud infrastructure and containerized deployments - Strong grasp of the fast-moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisions - 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 model benchmarking, experimentation frameworks, and cost or latency optimization at scale - Experience with vector databases, knowledge systems, and retrieval infrastructure - Background in startups or as a founder - Contributions to open-source or visible side projects in AI - Languages: Python, SQL, noSQL - Data / Platform: Vector databases, event-driven systems, APIs, observability tooling - LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw - Patterns: RAG, tool calling, agent workflows, eval pipelines - Infrastructure: Docker, Kubernetes, AWS / GCP / Azure - You do not need experience with every item, but this role will likely involve technologies such as: