Google Cloud & AI Platform Architect

Salary: $83,000 - 123,000 per year Requirements: Bachelors degree in computer science or a related field, or equivalent applicable work experience. 10+ years of experience in software development or architecture, including at least 3 years of individual-contributor technical leadership in platform or architecture roles. Deep knowledge of AI/ML ecosystems and experience designing enterprise-scale MLOps pipelines and AI Ops frameworks. Strong expertise in Google Cloud Platform and cloud-native architecture, with hands-on knowledge of GCP services and patterns for AI/ML platforms, MLOps, and CI/CD. Experience with multi-agent architecture, including orchestration patterns, tool or skill registries, memory and state management, and agent observability. Experience establishing technical standards and coordinating distributed engineering teams. Proven ability to lead platform architecture with cross-functional engineering partners and deliver enterprise-scale AI platform solutions. Ability to navigate novel technologies and ambiguity in evolving domains. Familiarity with security and compliance standards for platform operations. Strong analytical and problem-solving skills, with a data-driven approach. Excellent communication, program coordination, and stakeholder engagement skills, including presenting technical concepts to senior leaders and non-technical audiences. Responsibilities: Translate enterprise AI strategy into actionable platform roadmaps and technical priorities, balancing immediate needs with long-term scalability and reliability. Partner with data, cloud, security, infrastructure, product, engineering, and data science teams to define and deliver end-to-end AI architecture and operational strategies. Evaluate emerging technologies, platform options, and buy-versus-build decisions; recommend enhancements and optimize cost and performance. Design, implement, and scale cloud-based infrastructure on Google Cloud Platform and Databricks for internal and external applications. Architect AI/ML platform components, including data pipelines, model training and inference, orchestration, continuous training, deployment, monitoring, and observability. Establish architecture practices, patterns, standards, and controls for AI/ML development and deployment; ensure designs meet enterprise, regulatory, governance, privacy, and responsible AI requirements. Participate in Scale-lane Architecture Review Board decisions, advising on infrastructure fit, serving readiness, and cost-per-unit implications. Guide agentic platform evolution in partnership with the AI Architect Principal and AI Solution Architects, aligning work with organizational priorities and technology roadmaps. Mentor and coach AI architects, engineers, and AI Ops engineers through design reviews, technical guidance, and reusable patterns. Provide hands-on technical leadership, conduct design reviews, and uphold standards for code quality, testing, documentation, and maintainable engineering practices. Lead team development activities, including career growth, technical upskilling, performance reviews, hiring, onboarding, and team building. Oversee platform scalability, reliability, security, and cost optimization across cloud and on-premises environments. Implement monitoring and observability tools; define and track performance metrics such as uptime, latency, and resource utilization. Build and execute architecture audits, identify automation and process-improvement opportunities, and address platform performance issues and outages. Lead incident response and post-incident reviews, and apply relevant emerging technologies and industry practices to improve platform resilience and efficiency. Technologies: AI Architect CI/CD Cloud Databricks GCP MLOps Model Training Security Support Model Serving More: We are hiring a Senior Architect, Google Cloud Platform & AI Platform Architect, for Ultas AI Architecture pod to lead the scale lane for our core AI platforms. This is a senior architecture role with at least half of the work focused on hands-on platform design, infrastructure decisions, MLOps patterns, reviews, and standards. The role works across teams to shape platforms that are scalable, performant, reliable, and cost-efficient, while also providing technical mentorship and team leadership. last updated 41 week of 2026