Staff Software Engineer, Gen AI, Blackbelt Team

Job description

Work with the team to identify and qualify business opportunities, understand key customer technical objections, and develop the strategy to resolve technical blockers. Provide AI expertise to support the technical relationship with Google’s customers, manage product and solution briefings, create demos, proof-of-concept work, and partner directly with product management to prioritize solutions impacting customer adoption to Google Cloud. Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to implement a complete solution Google Cloud. Support developers, creators, and enterprises to leverage Google’s Generative Language APIs so they can build their own AI products in the future. Travel to customer sites, conferences, and other related events as needed. Minimum Qualifications: Bachelor's degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture. 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 2 years of experience with GenAI techniques (e.g., LLMs, multi-modal, large vision models) or with GenAI-related concepts (e.g., language modeling, computer vision). 2 years of experience as a Technical Solution Architect in a cloud computing environment, or in a customer-facing role. Preferred Qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. Experience building AI and machine learning solutions, machine learning operation frameworks like Kubeflow, and leveraging specific machine learning architectures (e.g., deep learning, LSTM, etc.). Understanding of AI models, large language models, and AI specialized infrastructure as it relates to AI trends and issues within businesses.