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Pliant is a Berlin-based European fintech specializing in B2B payment solutions. The company provides a modular, API-first platform that helps businesses streamline spending, improve cash flow, and integrate payments into financial workflows. Pliant serves over 4,000 businesses and 20+ partners globally, operating as a licensed e-money institution issuing Visa-powered credit cards across 11 currencies in 30+ countries.
The AI Experience team builds AI-powered product experiences that help finance teams understand, automate, and optimize company spending. This includes AI chat experiences, finance copilots, spend analytics, internal AI tooling, and autonomous workflows. The team focuses on building complex systems where LLMs are one component among many—structured financial data, APIs, retrieval, business logic, orchestration, evaluation, observability, and production infrastructure all play critical roles.
As an Applied AI Engineer, you will work at the intersection of software engineering, product thinking, and applied AI. You'll design and build AI-powered product experiences for finance and spend management, including chat experiences, copilots, autonomous workflows, and internal AI tools. You'll work with LLMs, structured financial data, APIs, and business logic to build production-grade AI systems, including retrieval systems (vector search, hybrid retrieval, text-to-SQL), OCR, and document understanding.
Key responsibilities include designing AI solutions using the right approach for each problem (tool calling, retrieval, agentic workflows, deterministic logic, or combinations), building observable and explainable systems with guardrails and quality monitoring, creating evaluation approaches to measure AI system improvements, experimenting with new models and techniques, and collaborating closely with product managers, designers, and engineers.
You should be comfortable with ambiguity and figuring out what is worth building, where AI helps, and where traditional software is better. The role requires balancing exploration of new AI capabilities with turning them into reliable, production-grade systems that customers can trust. You'll think carefully about accuracy, latency, cost, reliability, and user trust.
