Google AI Architect

Google AI Architect/AI and Engineering Join our AI & Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and re-engineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation. AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements. Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models-traditional teams, pools, or pods-are tailored to each client's needs, offering engineering-led advisory, implementation, and operational capabilities to accelerate innovation. Recruiting for this role ends on 10-31-2026 Work you'll do: • Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost. • Design, fine-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring. • Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. • Define end-to-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build. • Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce application and agentic design patterns. • Implement security and governance for AI/ML systems (data privacy, model poisoning, adversarial attacks); apply Gemini safety features and enterprise guardrails. Responsibilities include: • Architect and Design: Design and development of enterprise-grade AI applications and platforms, with a focus on scaling AI solutions for production. This includes defining the technical architecture, selecting appropriate technologies, and ensuring solutions are robust, scalable, and secure. • LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an emphasis on production-level performance and reliability. • Enterprise Architecture: Collaborate with enterprise architects to ensure AI solutions align with the broader company's technical strategy, governance, and standards. • Cloud and GenAI Native Development: Design and deploy applications using Cloud Native principles on a hyperscaler platform (AWS, Azure, GCP). Leverage a wide range of hyperscaler tools and services, including containers (Docker, Kubernetes), serverless functions, and managed databases. Should have experience in leveraging various GenAI tools to accelerate software development life cycle. • Security & Governance: Ensure the security of all AI/ML systems by addressing potential vulnerabilities such as data privacy concerns, model poisoning, and adversarial attacks. • Design Patterns: Apply and enforce Application Design Patterns and Agentic Design…