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 successfully implement a complete solution to customers to optimize Google Cloud effectiveness.
Lead the design, development, and iterative refinement of data-centric and AI-powered solutions on Google Cloud Platform (GCP), showcasing the potential of data and AI to address specific business needs.
Establish and promote innovative best practices and methodologies for AI-driven solutions, actively contributing to industry thought leadership through publications, presentations, and community engagement.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
6 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 engaging with, and presenting to, technical stakeholders and executive leaders.
Experience in programming/scripting languages such as Python.
Experience developing and deploying data solutions.
Preferred qualifications:
Experience translating ambiguous customer requirements into actionable AI roadmaps, defining the technical architecture for fine-tuning, RAG (Retrieval-Augmented Generation), and custom model development.
Experience in architecting and developing software or infrastructure for scalable, distributed systems.
Proven track record of managing stakeholder expectations and building consensus around complex AI initiatives, including navigating discussions on model ethics, bias, and return on investment (ROI).
Understanding of the AI/ML landscape, including familiarity with model evaluation frameworks, prompt engineering, and the integration of third-party foundational models.
