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9fin is an AI platform powering global debt markets, the world's largest asset class at over $145 trillion. The company centralizes proprietary credit data, analysis, and workflows across global markets, replacing fragmented data, PDFs, and manual processes. 9fin serves 300+ blue-chip institutions including global banks, asset managers, private equity firms, law firms, and advisors, with rapid expansion and strong product-market fit.
The Data Science team is expanding and seeking an Applied AI Manager to lead the application of AI across teams and products. This is a people-management role combining technical leadership with team development.
Key responsibilities include:
- Lead design and delivery of complex ML and GenAI systems, ensuring robustness, scalability, and business alignment
- Guide end-to-end model development from experimentation through deployment and monitoring, emphasizing reproducibility and reliability
- Drive architectural decisions for data-intensive, AI-driven applications, balancing innovation with production readiness
- Mentor and develop engineers across levels, sharing best practices in software engineering, ML/AI, and cloud infrastructure
- Collaborate cross-functionally to shape roadmaps, scope projects, and balance technical innovation with delivery
- Champion engineering excellence through coding standards, CI/CD pipelines, observability, and incident management
- Serve as a visible technical leader, advancing AI/ML adoption across the company and influencing strategy
Requirements:
- Proven experience leading engineering teams in high-growth, fast-paced environments
- Strong commitment to people development: mentoring, performance reviews, building inclusive high-performing teams
- Strong software engineering skills (Python preferred) combined with deep knowledge of AI/ML frameworks, GenAI tooling, and modern data/infrastructure stacks
- Hands-on experience building and deploying ML/GenAI systems in production, ideally in complex domains (finance, legal, or similar data-heavy spaces)
- Ability to lead architectural discussions and technical trade-offs while remaining hands-on
- Proficiency with cloud infrastructure (AWS preferred) and modern MLOps practices
- Thrives in collaborative, startup-style environments: adaptable, decisive, cross-functional, focused on business impact
