Senior Director, AI / Machine Learning Data Engineer
On-siteNew York City, New York, United States or New York, United States
New York City, New York, United States or New York, United StatesOn-siteFull TimeSenior LevelBachelors DegreeFinancial ServicesEnterprise
Full TimeSenior LevelBachelors DegreeEnterpriseFinancial Services
Job Summary
Lead solution architecture for frontier AI products by reviewing end-to-end technical designs and establishing reusable patterns for AI engineering and platform integration. Guide technical direction for new projects by aligning user needs with platform strategy, while acting as a senior advisor for complex cross-functional initiatives and key architecture reviews. Focus on defining secure, scalable products with an emphasis on reusability, resilience, and commercial viability. This role is located in New York, NY and requires 15+ years of software development experience.
Required Qualifications
- Strong experience designing and delivering enterprise software, platform and AI solutions in complex environments
- Deep understanding of AI engineering patterns
- Hands-on knowledge of AI/ML architecture across training, inference, feature pipelines, model serving, orchestration, agent protocols, etc
- Strong platform engineering skills across APIs, microservices, cloud infrastructure, CI/CD, observability, and secure deployment
- Experience working with AI platforms, developer platforms, or internal enablement products
- Ability to translate complex technical concepts into clear architecture decisions, delivery plans, and business outcomes, and ability to spot flaws and risks in designs
- Strong partnership and communication skills across engineering, product, risk, control, and senior stakeholders
- Bachelor's degree in computer science, engineering or a related discipline
- 15+ years of experience in software development
- experience in the securities or financial services industry
Desired Qualifications
- advanced degree
- Applied AI, ML/optimization, orchestration and software architecture, including evaluation frameworks and guardrail design
- Vector databases, retrieval systems, knowledge graphs, memory-based agents and agentic protocols
- MLOps, model lifecycle tooling, and production AI observability
- Experience with AI for engineering practices and agentic SDLC design, with an eye toward production scale and resilience
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