Staff AI Enablement Engineer

Trustly•San Francisco, CA

1d•$250,000 - $315,000•Remote

About The Position

The AI Enablement team sits at the intersection of technology, product, and people — responsible for accelerating Trustly's capacity to build and scale AI-powered capabilities across the organization. The team partners deeply with Engineering, Product, Data, and Operations to identify high-leverage opportunities, drive adoption of AI tooling, and establish the frameworks that let Trustly move faster and smarter. As a Staff AI Enablement Engineer, you'll be one of the most technically influential individual contributors at Trustly — part of the special forces team bringing agentic workflows and the productivity gains they unlock to every function in the company. Your users aren't just engineers: they're teammates in finance, legal, operations, sales, and beyond, all trying to figure out how to actually work with AI. You'll design and build the internal platforms, evaluation frameworks, and tooling that make it easier and faster for any team at Trustly to adopt agentic ways of working. This is a high-autonomy, high-ownership role for an experienced engineer who loves being in the weeds on hard technical problems and then scaling their solutions across an entire organization.

Requirements

8+ years of software engineering experience, with at least 3 years focused on AI/ML systems, LLM infrastructure, or applied ML in production environments.
Deep hands-on experience building with LLMs and modern AI stacks — including RAG, fine-tuning, prompt engineering, function calling, and multi-step agentic workflows.
Strong software engineering fundamentals: you write clean, production-grade code and design systems that are observable, testable, and maintainable.
Experience operating at Staff+ scope — driving technical decisions that span multiple teams, mentoring other engineers, and leading by influence rather than authority.
Track record of taking AI capabilities from prototype to production in a high-reliability environment — understanding the gap between a demo and something that runs at scale.
Comfortable with ambiguity: you can scope and execute on poorly-defined problems, communicate tradeoffs clearly, and move quickly without sacrificing quality.

Responsibilities

Design, build, and maintain Trustly's core AI platform — including LLM integration infrastructure, prompt management systems, evaluation pipelines, and agentic workflow tooling.
Partner with product and engineering teams across Trustly to embed AI capabilities into core products: payments, risk decisioning, fraud detection, developer tooling, and customer-facing surfaces.
Establish and own evaluation standards for AI systems — building robust testing frameworks that assess quality, safety, latency, cost, and reliability at scale.
Lead technical discovery and prototyping on high-ambiguity problems: evaluate new foundation models, agentic architectures, and toolchains; produce clear recommendations grounded in data.
Drive internal AI adoption by creating reusable libraries, patterns, documentation, and enablement resources that reduce the barrier for engineering teams to build with AI.
Collaborate with the VP of AI Enablement and cross-functional stakeholders to influence the technical roadmap and represent engineering constraints and opportunities in strategic conversations.