Applied AI Engineer
Company Overview
At Moss, we give finance professionals the power to automate their day-to-day and make forward-thinking decisions. Our culture is what makes that possible: we play to win, we obsess over quality, and we win as One Moss, and it works. Moss closed a €35 million Series C in August 2026, crossing a €1 billion valuation, and became one of Europe's fintech unicorns. Join us for what's next.
Moss is the Finance AI platform for Europe's mid-sized businesses, giving companies real-time visibility and full control over their spend. By automating card issuing, invoice management and expenses, Moss simplifies financial workflows and frees finance and accounting teams from manual, administrative work. Founded in Berlin and used by more than 10,000 businesses including Flink, Schufa, n8n and Auto1, Moss has raised €220+ million to date and operates in Germany, the Netherlands, the UK and further EU markets.
Job Summary
We are hiring an Applied AI Engineer to build AI agents and intelligent product capabilities that transform how finance teams work. This is a product engineering role - not an isolated prototyping or research role. You will own agent-based features end to end: from architecture and evaluation through backend integration, deployment, and operation in production.
Key Responsibilities
Build and Ship AI Agents
- Design and build agents that automate complex finance workflows.
- Own features from initial concept and prototype through production deployment.
- Integrate agents deeply with our backend services, APIs, data model, permissions, and product workflows.
- Design how agents securely access and use data from across the Moss platform.
- Turn emerging AI capabilities into reliable, customer-facing product features.
Evaluate and Improve Agent Performance
- Build systematic evaluations for agent quality, reliability, and business impact.
- Create representative test datasets, evaluation criteria, regression tests, and human-review processes.
- Measure and improve accuracy, latency, cost, and user experience.
- Establish observability and feedback loops that make agent behavior understandable and continuously improvable.
Develop the AI Application Architecture
- Apply context-engineering techniques such as RAG, MCP and knowledge graphs.
- Design prompts, tools, memory, workflows and orchestration strategies for production agents.
- Select and use appropriate orchestration frameworks, such as Google ADK, LangGraph, LangChain, LlamaIndex, or comparable technologies.
- Build appropriate guardrails, approval steps, and human-in-the-loop controls for sensitive financial workflows.
Integrate Machine-Learning Capabilities
- Collaborate with data scientists to integrate machine-learning models into our production systems.
- Build the services, data flows, APIs, and operational tooling required to make models usable within the product.
- Take responsibility for the production integration rather than handing prototypes to another engineering team.
Requirements / Qualifications
You are a seasoned software engineer with experience building and operating production applications. You are highly proficient in Python and/or Java and comfortable working across backend services, APIs, data, and application architecture.
- You have built and shipped at least one agent or LLM-powered product capability end to end.
- You have personally integrated agents into a broader product and backend architecture - not only developed standalone prototypes.
- You have practical experience evaluating agents or other non-deterministic AI systems.
- You understand how to provide agents with the right data and context while respecting security, permissions, and privacy.
- You have hands-on experience with prompt engineering, context engineering, and agent orchestration.
- You balance rapid experimentation with reliable, maintainable production engineering.
- You communicate clearly and collaborate effectively across engineering, product, and data science.
Relevant Technologies
You do not need experience with every technology below. We care most about strong engineering judgment and demonstrated end-to-end ownership.
- Agent orchestration: Google ADK, LangGraph, LangChain, LlamaIndex, or comparable frameworks
- Context engineering: RAG, MCP, knowledge graphs, tool use, memory, and retrieval systems
- AI evaluation: offline and online evaluations, test datasets, regression testing, observability, and human review
- Language models: Gemini, OpenAI, Anthropic, Llama, Mistral, or similar
- Backend engineering: Python or Java, REST APIs, Kafka, microservices, and distributed systems
- Data systems: SQL, PostgreSQL, BigQuery, vector search, and data pipelines
- Cloud AI platforms: GCP and Vertex AI, or comparable platforms
Salary & Benefits
Top-of-market compensation package, including equity.
Our vibrant offices are at the heart of our culture, where in-person time fuels collaboration and connection over weekly breakfasts and Friday demos.
Additional benefits include:
- 20 days “work from abroad”
- 600EUR/GBP Learning & Development Budget
- Other local benefits
Unless stated otherwise, benefits apply to full-time positions (interns and working students receive a tailored package).
Location
Berlin, Berlin, Germany (Hybrid)
Employment Type
Full-time
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