About the Role
As a Generative AI Vice President within our client's organization, you will lead the development, scalability, and operational excellence of LLM-powered AI products. This role sits at the intersection of advanced research and enterprise-grade engineering, driving the design of reusable backend APIs and scalable AI systems that deliver measurable business impact.
You will partner closely with cross-functional teams—including ML Center of Excellence, AI Research, and Cloud Engineering—to accelerate innovation and bring high-performing, production-ready AI solutions to market. A key focus will be ensuring that all APIs and platforms are built for scale, reliability, and reusability, enabling internal teams to efficiently develop and deploy their own ML-driven products.
By enforcing strong architectural principles such as separation of concerns and well-defined interfaces, you will help cultivate a high-leverage ecosystem of AI capabilities across the organization.
Key Responsibilities
- Leverage large-scale data assets alongside cutting-edge AI technologies, including LLMs and multimodal models, to build impactful solutions
- Bridge the gap between AI research and production engineering, translating advanced concepts into scalable systems
- Lead the design, architecture, and delivery of production-grade AI platforms in close collaboration with Cloud Engineering and SRE teams
- Drive best practices for scalability, performance, and reliability across ML services and APIs
- Champion reusable frameworks and API-first development to accelerate AI adoption across teams
Required Qualifications
- PhD in a quantitative field such as Computer Science, Mathematics, or Statistics
- Hands-on experience as an individual contributor in ML engineering environments
- Proven success building and leading high-performing teams of ML engineers and scientists
- Strong foundation in statistics, optimization, and machine learning theory, with a focus on NLP and/or Computer Vision
- Strong experience with AWS, Kubernetes, and Fargate
- Experience developing scalable, distributed systems (e.g., Ray, Horovod, DeepSpeed)
- Demonstrated ability to align technical solutions with business goals and define clear OKRs
- Experience owning and operating ML services in enterprise environments
- Deep understanding of computer science fundamentals and SDLC best practices
- Strong ability to translate business objectives into well-defined ML problems
- Excellent communication skills, with the ability to influence stakeholders at all levels
Preferred Qualifications
- Experience designing and orchestrating ML pipelines using DAG-based tools (e.g., Kubeflow, DVC, Ray)
- Expertise in building batch and real-time microservices exposed via gRPC and/or GraphQL
- Hands-on experience with parameter-efficient fine-tuning, model quantization, and optimization techniques for LLMs
- Familiarity with advanced prompting strategies such as Chain-of-Thought, Tree-of-Thought, and Graph-of-Thought