Senior AI Engineer

Role overview

Join Finom as a Senior AI Engineer to design, build, and operate production-grade AI systems that solve real business problems. This hands-on role spans multiple domains including onboarding, customer support, AI accounting, fraud detection, and internal automation.

Responsibilities

• Own AI systems end-to-end: problem framing, architecture, implementation, evaluation, deployment, monitoring, and iteration.
• Design and integrate AI into real workflows by partnering with solution managers, domain teams, and engineers.
• Move solutions from prototype to production with meaningful evaluation and monitoring.
• Contribute to AI platform and tooling decisions.
• Continuously experiment with and assess new AI models, tools, and agentic patterns for production fit.

Requirements (Must-haves)

• Strong ability to translate ambiguous business problems into robust technical solutions.
• Hands-on LLM application experience, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning.
• Proven experience building and deploying AI systems in production.
• Strong Python and software engineering fundamentals; a track record of shipping production systems.
• Ability to create evaluation, monitoring, and continuous improvement loops.
• Product mindset focused on user outcomes and real business impact.
• Comfort across the full AI lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support.
• Autonomy, pragmatism, clear communication, and ability to work through ambiguity.

Nice-to-haves / Additional experience

• Experience integrating AI into backend or product workflows.
• Cloud and deployment experience with containerized infrastructure.
• Background in fintech, financial services, risk, compliance, or operations-heavy environments.
• Applied ML experience beyond LLMs (e.g., classification, anomaly detection, ranking, document intelligence).
• Experience with benchmarking/experimentation frameworks and cost/latency optimization.
• Knowledge of vector databases, retrieval/knowledge systems, and related infrastructure.
• Startup background or founder experience; open-source or visible AI projects.

Tech areas you may work with

• Python, SQL and some NoSQL
• RAG, tool calling, agent workflows, eval pipelines
• Vector databases, event-driven systems, APIs, observability
• Docker, Kubernetes, AWS/GCP/Azure
• OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch (and related tooling)