Staff AI Enablement Engineer

Trustly•San Francisco, CA

About The Position

The Staff AI Enablement Engineer is a highly autonomous, technically influential individual contributor on Trustly’s AI Enablement team. The team works across technology, product, and people to accelerate the development and adoption of AI-powered capabilities throughout the organization. The engineer will build internal platforms, evaluation frameworks, and tools that help teams across engineering and business functions adopt agentic workflows. The role combines hands-on engineering on challenging technical problems with scaling solutions across the organization. It involves partnering with teams across Trustly to bring AI capabilities into products and workflows, setting standards for AI evaluation, and helping shape the technical roadmap in collaboration with the VP of AI Enablement and other stakeholders.

Requirements

At least 8 years of software engineering experience, including at least 3 years focused on AI/ML systems, LLM infrastructure, or applied ML in production environments.
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, including the ability to write production-grade code and design observable, testable, and maintainable systems.
Experience operating at Staff+ scope, driving technical decisions across multiple teams, mentoring engineers, and leading through influence.
A track record of taking AI capabilities from prototype to production in a high-reliability environment and scaling them beyond demos.
Ability to scope and execute poorly defined problems, communicate tradeoffs clearly, and move quickly without sacrificing quality.

Responsibilities

Design, build, and maintain Trustly’s AI platform, including LLM integration infrastructure, prompt management systems, evaluation pipelines, and agentic workflow tooling.
Partner with product and engineering teams to embed AI capabilities into payments, risk decisioning, fraud detection, developer tooling, and customer-facing products.
Establish and own evaluation standards and testing frameworks for AI systems, assessing quality, safety, latency, cost, and reliability at scale.
Lead technical discovery and prototyping for ambiguous problems; evaluate foundation models, agentic architectures, and toolchains, and provide data-grounded recommendations.
Drive internal AI adoption by creating reusable libraries, patterns, documentation, and enablement resources.
Collaborate with the VP of AI Enablement and cross-functional stakeholders to influence the technical roadmap and represent engineering constraints and opportunities in strategic discussions.