Senior AI Engineer
hace 18 horas
Barcelona
• We are looking for a Senior AI Engineer to design, build, and operate AI systems that solve real business problems across Finom, • 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, • 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, • 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, • 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, • 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, • A strong software engineer with deep Python experience and a track record of shipping production systems, • Hands‑on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine‑tuning, • Autonomous, pragmatic, and able to keep momentum without heavy supervision, • Clear in communication and comfortable working across functions, • Experience with cloud infrastructure and containerized deployments, • Fluent English, • Ability to design meaningful evaluation, monitoring, and continuous improvement loops, • Proven experience building and deploying AI systems in production, • Strong grasp of the fast‑moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisions, • Someone who actively keeps up with the fast‑moving AI landscape and can separate hype from what is actually useful, • Product‑minded and focused on real user outcomes, not just model outputs, • Curious, proactive, low‑ego, and biased toward action, • Strong at turning ambiguous business problems into robust technical solutions, • Actively experiments with new AI models, tools, and agentic patterns, and can evaluate which approaches are worth productionizing, • Experience integrating AI systems into backend or product workflows, • Strong Python and software engineering fundamentals, • Comfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support, • Strong ownership mindset and ability to work through ambiguity, • 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 #J-18808-Ljbffr