Director & Chief/Principal AI Architect

Fraud Prevention & AML Platform (Series-C)

$250,000-$270,000 base + Stock Options (Potential Flexibility)

Remote Flexible

What You’ll Do

• Work together with the Architecture team to define SEON’s AI architectural vision and long-term roadmap
• 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 Design: 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, EKS, 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.