AI Incubation Lead, VertexAI, Core, Global Services Delivery

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience supporting customers across industries and business functions, leading programs in enterprise implementations including technical support, escalation management, consulting, and issue resolution.
• Experience in one or more discipline: vLLM, Slurm, Pallas, JAX, machine learning, recommendation systems, natural language processing, computer vision, pattern recognition, or artificial intelligence.
• Experience managing, scaling, and delivering projects with tight deadlines as well as a technical team.

Preferred qualifications:

• Certification in cloud, such as the Google Cloud Platform (GCP) Machine Learning Engineer.
• Experience in AI operations, including implementing, migrating, and supporting live AI environments on a public cloud.
• Experience in a statistical programming language like R or Python and with applied machine learning techniques.
• Ability to architect and explain ML Operations platforms and translate business problems into viable AI projects.
• Excellent communication, writing, presentation, and problem-solving skills.

About the job

The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

In this role, you will help bring to the market the Vertex AI Core strategy globally as well as organize and lead a cross-functional focused team of experts delivering early stage pilots which address business needs leveraging Google Cloud's. You will work closely with internal Google Cloud AI teams (e.g., product, engineering, business, professional services, learning, etc.) and our external customers to define, align, and execute specialized pilots aiming to both help guide our product strategy as well as transform customers via implementation of Vertex AI core products from design to deployment and optimization. You will develop technical leadership and collaborative technical teams working together to solve customer business problems and will navigate ambiguity, have experience working with AI/ML on cloud technologies,technical depth and work with a set of client and internal stakeholders. You will have excellent communication and stakeholder management skills and will be highly motivated, comfortable in a fast-changing atmosphere, have a strong desire to learn, and, love working with Google products.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $147,000-$216,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Implement, migrate, and support live AI environments on a public cloud, ensuring adherence to core production strategies.
• Deploy and manage machine learning frameworks such as Slurm, Ray.io, Kubeflow, or MLFlow.
• Design and architect machine learning (ML) Operations platforms and clearly articulate their functionality to stakeholders.
• Translate complex business problems into viable AI projects and establish metrics to measure their return on investment (ROI).
• Collaborate effectively with cross-functional teams and stakeholders using communication, presentation, and problem-solving skills.