Applied AI Engineer

The role

An Applied AI Engineer will build AI agents and intelligent product capabilities. This product engineering role involves owning agent-based features from architecture and evaluation through backend integration, deployment, and production operation.

What you will do

• Design and build agents that automate complex finance workflows.
• Own features from initial concept and prototype through production deployment.
• Integrate agents with backend services, APIs, data model, permissions, and product workflows.
• Design secure data access for agents across the platform.
• Turn emerging AI capabilities into reliable, customer-facing product features.
• Build systematic evaluations for agent quality, reliability, and business impact.
• Create test datasets, evaluation criteria, regression tests, and human-review processes.
• Measure and improve accuracy, latency, cost, and user experience.
• Establish observability and feedback loops for agent behavior.
• Design prompts, tools, memory, workflows and orchestration strategies.

Required qualifications

• Seasoned software engineer with experience building and operating production applications.
• Highly proficient in Python and/or Java.
• Comfortable working across backend services, APIs, data, and application architecture.
• Built and shipped at least one agent or LLM-powered product capability end to end.
• Personally integrated agents into a broader product and backend architecture.
• Practical experience evaluating agents or other non-deterministic AI systems.
• Understand how to provide agents with the right data and context while respecting security, permissions, and privacy.
• Hands-on experience with prompt engineering, context engineering, and agent orchestration.

Nice to have

• 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

Benefits

• Top-of-market compensation package, including equity.
• Weekly breakfasts and Friday demos.
• 20 days work from abroad.
• 600EUR/GBP Learning & Development Budget.
• Other local benefits.

About the company

Moss is a Finance AI platform for Europe's mid-sized businesses that automates card issuing, invoice management and expenses. The company is a fintech unicorn with over 10,000 business customers.