Drive digital transformation by leveraging Google Cloud's enterprise-grade solutions and cutting-edge technology. Partner with organizations globally to enhance productivity, mobility, and collaboration through cloud computing. Solve complex technical challenges for customers and collaborate with product teams to innovate and stay ahead of industry trends.

The APAC Google Forge Team collaborates closely with customers to understand their unique challenges and aspirations. We then design and build practical, working solutions powered by data, analytics, AI, and software engineering to demonstrate tangible business impact.

Lead the creation and development of advanced prototypes, showcasing the power of data, analytics, AI, and software engineering in tackling complex business issues for clients. Mentor team members and influence the strategic direction of our solution-building initiatives.

Take ownership of the technical relationship with partners, guiding them through successful pilots and proof-of-concept phases. Facilitate customer acceptance of technical proposals, leading to agreements, and support partners during the migration phase to ensure seamless and successful deployments.

Build strong, trusted advisor relationships, offering expert recommendations on integration strategies, enterprise architectures, platforms, and application infrastructure to optimize Google Cloud effectiveness for clients.

Establish and champion innovative best practices for AI-driven solutions. Contribute to industry thought leadership through publications, presentations, and active community engagement.

Possess a Bachelor's degree or equivalent practical experience, coupled with at least 6 years of experience in cloud computing. Your expertise should specifically encompass machine learning architecture, software development, and model deployment within a customer-facing or consulting capacity.

Demonstrated experience in engaging with and presenting to both technical stakeholders and executive leadership is crucial. Proficiency in programming/scripting languages like Python is required, alongside a solid background in developing and deploying data solutions.

Preferred qualifications include translating ambiguous customer requirements into actionable AI roadmaps, defining technical architectures for fine-tuning, RAG, and custom model development. Experience in architecting and developing software or infrastructure for scalable, distributed systems is highly valued. A proven ability to manage stakeholder expectations and build consensus around complex AI initiatives, including discussions on ethics, bias, and ROI, is also a significant advantage. Familiarity with the AI/ML landscape, including model evaluation frameworks, prompt engineering, and integrating third-party foundation models, will set candidates apart.