When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.
In this role, you will lead the design and development of prototypes, demonstrating the power of data, analytics, AI, and software engineering to solve complex business challenges for our customers. You will play a key role in mentoring team members and contributing to the strategic direction of our solution-building efforts.
The APAC Google Forge team partners with customers to bring their visions to life. We collaborate closely to understand their challenges and ideas, then design and build practical working solutions using data, analytics, AI, and software engineering. Our goal is to demonstrate how these technologies can work together to achieve business results.
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.
Responsibilities
- Own the technical relationship with partners, empowering them to drive a successful pilot or proof-of-concept, support partners drive toward customer acceptance of the technical proposal, leading to an agreement, and work with partners during the migration phase to assure they have all the tools necessary to deliver a successful deployment.
- Build trusted advisory relationships and make recommendations on integration strategies, enterprise architectures, platforms, and application infrastructure required to implement a complete solution providing best practice advice to customers to enhance Google Cloud effectiveness.
- Lead the design, development, and iterative refinement of data-centric and AI-powered solutions on Google Cloud Platform, showcasing the potential of data and AI to address specific business needs.
- Establish and promote innovative best practices and methodologies for AI-driven solutions, contributing to industry thought leadership through publications, presentations, and community engagement.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, AI, Mathematics, a related technical field, or equivalent practical experience.
- 6 years of experience in software engineering or cloud computing, including 3 years of experience in machine learning systems design and managing the AI model lifecycle.
- Experience in application development and DevOps practices, including CI/CD, containerization (e.g., Docker or Kubernetes), and infrastructure as code.
- Experience in developing and deploying AI solutions using machine learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
- Experience engaging with, and presenting to, technical stakeholders and executive leaders.
Preferred qualifications:
- 10 years of experience in cloud computing, with a focus on machine learning architecture, software development and model deployment in a customer-facing or consulting role.
- Experience in architecting and developing software or infrastructure for distributed systems.
- Experience managing stakeholder expectations and building consensus around AI initiatives.
- Understanding of the AI/ML landscape, including knowledge of model evaluation frameworks, prompt engineering, and the integration of third-party foundational models.
- Ability to translate customer requirements into AI roadmaps, defining the technical architecture for fine-tuning, Retrieval-Augmented Generation (RAG), and custom model development.