We are thrilled to introduce you to our team at the Chief Data and Analytics Office (CDAO) organization. As the driving force behind the firmwide adoption of artificial intelligence (AI) across our company, our dedicated team is responsible for overseeing data use, governance, and controls around the build, adoption and maintenance of cloud infrastructure, data and AI/ML products. With a focus on both effectiveness and responsibility, we strive to push the boundaries of innovation while ensuring ethical and sustainable practices. Join us on this exciting journey as we revolutionize the way we leverage data and analytics to shape the future of our organization.

As a Generative AI Vice President within our CDAO organization, you will play a crucial role in ensuring the smooth operation and optimization of our LLM aided AI products. Our firm-wide team focuses on developing scalable LLM-based products and reusable back-end APIs. You will engage in close collaboration with cross-functional teams, including the ML Centre of Excellence, AI Research, Cloud Engineering, and others, to foster innovation and deliver solutions that yield a high Return-on-Investment (RoI). You will ensure that our APIs are built with scalability in mind, allowing them to efficiently handle a large number of requests without compromising performance. By designing APIs with a clear separation of concerns and well-defined interfaces, we enable other teams and developers to leverage our APIs to build their own ML products and solutions, fostering a culture of collaboration and efficiency. 

 

Job Responsibilities 

Combine vast data assets with cutting-edge AI, including LLMs and Multimodal LLMs Bridge scientific research and software engineering, requiring expertise in both domains Collaborate closely with cloud and SRE teams while leading the design and delivery of production architectures

 

Required qualifications, capabilities, and skills 

PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics. Experience in an individual contributor role in ML engineering. Proven track record in building and leading teams of experienced ML engineers/scientists. Solid understanding of the fundamentals of statistics, optimization, and ML theory, focusing on NLP and/or Computer Vision algorithms. Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.). Ability to understand and align with business expectations, and write clear and concise OKRs (Objectives and Key Results). Experience as a "Responsible Owner" for ML services in enterprise environments. Excellent grasp of computer science fundamentals and SDLC best practices. Ability to understand business objectives and align ML problem definition. Strong communication skills to effectively convey technical information and ideas at all levels, building trust with stakeholders.

 

Preferred qualifications, capabilities, and skills 

Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray). Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints. Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models. Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.