Summary
✨ AI‑Generated
Lead the architecture and delivery of enterprise-grade generative AI platforms as a Senior Platform Engineer. You will help move AI initiatives from experimentation into production by designing secure, high-performance, and cost-optimized environments. Responsibilities include deploying and fine-tuning advanced generative AI models, managing prompts and model versions, and building orchestration and agentic logic using cloud-based AI technologies.
Highlights
Lead the architecture and delivery of enterprise-grade generative AI platforms. The role provides hands-on opportunities to work with Google Cloud technologies, advanced AI models, model lifecycle management, orchestration, and secure, high-performance, cost-optimized enterprise environments.
Description
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+ years specifically focused on AI/ML implementation and deploying agentic systems
Proven track record of delivering end-to-end AI solutionsfor enterprise clients.
Why this role?
In this role, you aren't just an "AI developer"—you are a Reinvention Engineer.
You are building the "Enterprise AI foundation” for our clients across business verticals.
You will be the technical lead who ensures that when our clients deploy multi-agent systems, the infrastructure is as robust, secure, and performant as their core banking or ERP systems.
