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

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

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

Finom is hiring a Senior AI Engineer (Remote)