Solutions Acceleration Architect, Google Cloud

Google Google · Big Tech · Bengaluru, Karnataka, India +2

This role focuses on designing and developing AI-powered solutions and prototypes for enterprise customers on Google Cloud Platform. The Solutions Acceleration Architect will lead technical relationships with partners, build trusted advisory relationships, and define technical architectures for AI initiatives including fine-tuning and RAG. The role involves showcasing the potential of data and AI to solve business challenges and contributing to industry thought leadership.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. Establish and promote innovative best practices and methodologies for AI-driven solutions, actively contributing to industry thought leadership through publications, presentations, and community engagement.

Skills

Required

  • Python
  • cloud computing
  • machine learning architecture
  • software development
  • model deployment
  • customer-facing or consulting role
  • technical stakeholders and executive leaders
  • data solutions

Nice to have

  • fine-tuning
  • RAG (Retrieval-Augmented Generation)
  • custom model development
  • architecting and developing software or infrastructure for scalable, distributed systems
  • managing stakeholder expectations
  • model ethics
  • bias
  • return on investment (ROI)
  • model evaluation frameworks
  • prompt engineering
  • integration of third-party foundational models

What the JD emphasized

  • 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 translating ambiguous customer requirements into actionable AI roadmaps, defining the technical architecture for fine-tuning, RAG (Retrieval-Augmented Generation), and custom model development.
  • 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).

Other signals

  • design and development of cutting-edge prototypes
  • demonstrating the transformative power of data, analytics, AI, and software engineering
  • architecting and developing software or infrastructure for scalable, distributed systems
  • translating ambiguous customer requirements into actionable AI roadmaps
  • defining the technical architecture for fine-tuning, RAG (Retrieval-Augmented Generation), and custom model development