Join us as we revolutionize the future of data and AI at our organization. As a Generative AI Vice President in the Chief Data and Analytics Office, you will play a pivotal role in shaping firmwide adoption of artificial intelligence. Collaborate with top talent across cloud, engineering, and research teams to deliver impactful solutions. Your work will directly influence how we build, scale, and optimize advanced AI/ML products. Be part of a team that values creativity, ethical practices, and continuous growth—where your expertise drives real business outcomes.

As a Generative AI Vice President in the Chief Data and Analytics Office, you will lead us in developing scalable LLM-based products and reusable APIs. You will collaborate with cross-functional teams to drive innovation and deliver high-impact solutions. Your role empowers you to shape our AI/ML strategy, optimize performance, and foster a culture of collaboration. We value your expertise in both technical and business domains, ensuring you have the opportunity to make a significant impact.

Job Responsibilities

Lead the design and delivery of production architectures for AI and ML products Combine vast data assets with advanced AI, including LLMs and Multimodal LLMs Bridge scientific research and software engineering to deliver robust solutions Collaborate with cloud and SRE teams to optimize system performance Develop scalable APIs with clear separation of concerns and well-defined interfaces Foster innovation and continuous improvement across cross-functional teams Ensure solutions meet business objectives and deliver high Return-on-Investment Communicate technical concepts effectively to diverse audiences Align ML product development with organizational goals Mentor and guide teams of ML engineers and scientists Champion ethical and sustainable AI practices

Required Qualifications, Capabilities, and Skills

PhD in Computer Science, Mathematics, Statistics, or related quantitative discipline Minimum 5 years as an individual contributor in ML engineering Proven experience working with teams of ML engineers or scientists Strong foundation in statistics, optimization, and ML theory (NLP and/or Computer Vision) Hands-on experience with agent based workflows, MCP, RAG and context engineering. Ability to write clear and concise OKRs and align with business expectations Experience as a responsible owner for ML services in enterprise environments Excellent grasp of computer science fundamentals and SDLC best practices Ability to align ML problem definition with business objectives Strong communication skills for technical and non-technical stakeholders  Demonstrated ability to build trust and foster collaboration

Preferred Qualifications, Capabilities, and Skills

Hands-on experience with distributed, multi-threaded, scalable applications (Ray, Horovod, DeepSpeed) Experience designing and implementing pipelines using DAGs (Kubeflow, DVC, Ray) Ability to construct batch and streaming microservices with gRPC and/or GraphQL endpoints Demonstrated expertise in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLMs  Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, and Graph-of-Thoughts prompting strategies