AI Principal Engineer / Architect

AI Principal Engineer / Architect

SEON is the leading fraud prevention system of record, catching fraud before it happens at any point across the customer journey. Trusted by over 5,000 global companies, we combine your company’s data with our proprietary real-time signals to deliver actionable fraud insights tailored to your business outcomes. We deliver the fastest time to value in the market through a single API call, enabling quick and seamless onboarding and integration. By analyzing billions of transactions, we’ve prevented $200 billion in fraudulent activities, showcasing why the world’s most innovative companies choose SEON.

We are looking for an exceptional AI Principal Engineer / Architect to guide the evolution of SEON's AI and ML architecture. You will serve as the technical visionary and hands-on leader for our AI strategy, setting direction for model lifecycle management, infrastructure design, and cross-functional integrations with product, platform, and engineering.

This role is critical in ensuring that SEON’s fraud detection models remain cutting-edge, scalable, and deeply integrated into our core systems. You will work across domains — from real-time inference pipelines and feature stores to advanced anomaly detection and LLM-enabled risk workflows — helping us push the boundaries of what’s possible in digital fraud prevention.

This role offers flexibility and can be based remotely in the EU.

What You’ll Do

• Architect and Scale AI Systems: Design the foundational architecture for GenAI-powered fraud detection — from prompt pipelines and embeddings to real-time enrichment and scoring services.
• Lead GenAI Product Integration: Partner with product and engineering teams to build and launch features that leverage LLMs and generative techniques to detect fraud signals, surface insights, and enhance user workflows.
• Develop Reusable Components: Build reusable infrastructure and SDKs for LLM integration, prompt templating, retrieval-augmented generation (RAG), and online feature inference.
• Own AI Infrastructure: Define patterns and tooling for model lifecycle, experimentation, evaluation, versioning, deployment, and monitoring using an AWS-native stack (e.g., SageMaker, BedRock, etc.).
• Embed AI in the Platform: Drive seamless integration of generative and traditional ML capabilities into SEON’s core SaaS product, with a focus on real-time responsiveness and usability.
• Collaborate Cross-Functionally: Act as a trusted technical partner to product managers, fraud experts, and customer-facing teams — shaping the roadmap for AI-first product features.
• Champion Engineering Standards: Set the bar for high-quality, reliable AI systems through testing, CI/CD integration, data validation, and observability practices.
• Explore and Prototype: Stay on the cutting edge of LLM tools, open-source models (e.g., Llama, Mistral, Claude), and vector stores — and rapidly prototype ideas to test real-world utility.

What You Bring

• Generative AI Experience: Solid understanding of LLM architecture, prompt engineering, embeddings, vector search (e.g., FAISS, pgvector), and GenAI product patterns like RAG or tool use.
• Product-Oriented Mindset: A strong belief that AI is only valuable when it solves real user problems — with a bias toward simplicity, reliability, and performance.
• ML Engineering Expertise: 8+ years of experience building AI/ML systems at scale, ideally in a SaaS, B2B or data-heavy product environment.
• AWS-Native Thinking: Proficiency in designing AI/ML infrastructure on AWS (SageMaker, S3, Lambda, API Gateway, Step Functions, etc.).
• System Design Strength: Ability to define architecture that balances latency, scale, experimentation, and cost — with a deep understanding of distributed systems.
• Full-Stack AI Lifecycle: Familiarity with the end-to-end AI development process — from prototyping and evaluation to deployment and monitoring.
• Collaboration and Leadership: Experience working cross-functionally and mentoring other engineers or data scientists to deliver AI features that make it to production.
• Fraud, Risk or Fintech Curiosity (a plus): Experience in domains like fraud detection, fintech, transaction monitoring, or security is a bonus — but a sharp learning curve is just as welcome.
• Masters or PhD - Data Science is preferable.