Senior Gemini Platform Engineer

This is a Senior Gemini Platform Engineer role with Accenture based in Barangaroo, NSW, AU == Accenture == Role Seniority - senior More about the Senior Gemini Platform Engineer role at Accenture Accenture is a global professional services company with leading capabilities in digital, cloud and security. Find out more about us at accenture.com . Role Focus You will lead the technical architecture and delivery of enterprise-grade AI platforms. Your mission is to move clients from "Experimentation" to "Production" by architecting high-performance, secure, and cost-optimized environments for Gemini models. You will be responsible for architecting and implementing Google cloud, Gemini Enterprise, Vertex AI and other Google specific technology focused solutions for Accenture’s clients. Key Responsibilities & Toolset Proficiency Generative AI & Model Management Gemini 1.5 Pro/Flash & Model Garden: Deploy and fine-tune Gemini models for specialized tasks (reasoning, long-context analysis, multimodal processing). Vertex AI Studio: Manage the lifecycle of prompts and model versions, ensuring optimal performance across different enterprise use cases. Orchestration & Agentic Logic Vertex AI Agent Builder: Build "System-of-Action" agents using the native Google stack to minimize latency and maximize security. Multi-Agent Systems: Architect complex, stateful agentic workflows, utilizing Google’s solutions and other relevant industry leading solutions to solve complex business use-cases spanning multiple industries. Agent Development Kit (ADK): Standardize the creation of agents to ensure portability and consistency across the enterprise. Data & Vector Infrastructure BigQuery (Vector Search): Integrate structured business data with unstructured vector embeddings directly within BigQuery to power grounded, real-time AI responses. Vertex AI Search & Conversation: Implement RAG (Retrieval-Augmented Generation) at scale, ensuring agents have access to the most recent and relevant enterprise knowledge. Cloud Architecture & Engineering Microservices (Cloud Run/GKE): Containerize and scale agentic applications using GKE for high-performance workloads or Cloud Run for serverless efficiency. Event-Driven Design (Pub/Sub): Build asynchronous, resilient AI pipelines that trigger actions across the enterprise based on real-time data events. Development & MLOps Python & API Design: Craft robust, clean, and performant Python code and design secure APIs that connect Gemini to legacy systems (ServiceNow, SAP, Oracle). CI/CD for ML (MLOps): Implement automated testing, deployment, and monitoring pipelines to manage model drift and ensure reliability. Productivity Tools: Leverage Gemini Code Assist , Gemini CLI , and Antigravity to accelerate the development lifecycle and automate repetitive infrastructure tasks. Technical Requirements Platform Mastery: Advanced experience with the Vertex AI suite, including Model Garden and Agent Builder. Experience with Gemini CLI, Code Assist, Agent development kit and the future breadth of Google development tools. Engineering Excellence: Proven track record of designing and deploying AI and Agentic frameworks, architecting agentic workflows compliant with responsible AI guideline. Sound knowledge of Google cloud solutions including Google Kubernetes engine and other relevant tools. MLOps Discipline: Hands-on experience with Vertex AI Pipelines or Kubeflow for managing production AI lifecycles. Data Savvy: Proficiency in SQL/BigQuery and vector database management. Security Mindset: Deep understanding of Google Cloud VPC Service Controls , IAM, RAI and enterprise security protocols for AI. Qualifications 5+ years in cloud engineering, Machine learning and AI development. 3+