The ad says“Patterns: RAG, tool calling, agent workflows, eval pipelines”
The ad says“Experience with applied ML beyond LLMs, such as classification, anomaly detection, ranking, or document intelligence”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“Data / Platform: Vector databases, event-driven systems, APIs, observability tooling”
The ad says“Infrastructure: Docker, Kubernetes, AWS / GCP / Azure”
The ad says“Infrastructure: Docker, Kubernetes, AWS / GCP / Azure”
The ad says“Experience with applied ML beyond LLMs, such as classification, anomaly detection, ranking, or document intelligence”
The ad says“Infrastructure: Docker, Kubernetes, AWS / GCP / Azure”
The ad says“Experience with applied ML beyond LLMs, such as classification, anomaly detection, ranking, or document intelligence”
The ad says“Patterns: RAG, tool calling, agent workflows, eval pipelines”
The ad says“Data / Platform: Vector databases, event-driven systems, APIs, observability tooling”
The ad says“Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning”
The ad says“Infrastructure: Docker, Kubernetes, AWS / GCP / Azure”
The ad says“Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“Infrastructure: Docker, Kubernetes, AWS / GCP / Azure”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“Experience with model benchmarking, experimentation frameworks, and cost or latency optimization at scale”
The ad says“Data / Platform: Vector databases, event-driven systems, APIs, observability tooling”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning”
The ad says“LLM / AI: OpenAI, Anthropic, LangGraph, Hugging Face, Ollama, PyTorch, OpenClaw”
The ad says“Patterns: RAG, tool calling, agent workflows, eval pipelines”
The ad says“Experience with applied ML beyond LLMs, such as classification, anomaly detection, ranking, or document intelligence”
The ad says“Comfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support”
The ad says“Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning”
The ad says“Patterns: RAG, tool calling, agent workflows, eval pipelines”
The ad says“Data / Platform: Vector databases, event-driven systems, APIs, observability tooling”
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
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
You do not need experience with every item, but this role will likely involve technologies such as:
Data / Platform: Vector databases, event-driven systems, APIs, observability tooling
Preferred experience
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
Experience integrating AI systems into backend or product workflows
Hands-on experience with LLM applications, including some of: RAG, tool use, agents, prompt engineering, evals, structured outputs, guardrails, or fine-tuning
Strong at turning ambiguous business problems into robust technical solutions
A strong software engineer with deep Python experience and a track record of shipping production systems
Actively experiments with new AI models, tools, and agentic patterns, and can evaluate which approaches are worth productionizing
Comfortable across the full lifecycle: prompting, retrieval, experimentation, evaluation, deployment, and production support
Fluent English
Experience with cloud infrastructure and containerized deployments
Proven experience building and deploying AI systems in production
Clear in communication and comfortable working across functions
Strong Python and software engineering fundamentals
Curious, proactive, low-ego, and biased toward action
Ability to design meaningful evaluation, monitoring, and continuous improvement loops
Autonomous, pragmatic, and able to keep momentum without heavy supervision
Strong grasp of the fast-moving AI landscape, with the ability to turn relevant advances into practical product and engineering decisions
Strong ownership mindset and ability to work through ambiguity
Product-minded and focused on real user outcomes, not just model outputs
Someone who actively keeps up with the fast-moving AI landscape and can separate hype from what is actually useful
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
