Google AI Lead Architect

Deloitte Deloitte · Consulting

Lead architect role focused on designing, building, and deploying enterprise AI platforms and applications on Google Cloud, specifically leveraging Vertex AI and Gemini. The role involves architecting AI solutions, fine-tuning LLMs, building RAG and agentic systems, defining end-to-end architectures including data pipelines and MLOps, leading cloud-native development, and implementing security and governance for AI/ML systems.

What you'd actually do

  1. Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost.
  2. 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.
  3. Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability.
  4. 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.
  5. Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce application and agentic design patterns.

Skills

Required

  • Google Cloud Platform (GCP)
  • Vertex AI
  • Gemini
  • LLM fine-tuning
  • LLM evaluation
  • LLM governance
  • prompt engineering
  • tool/function calling
  • safety policies
  • Vector Search
  • RAG
  • agentic solutions
  • context management
  • retrieval strategies
  • observability
  • data pipelines
  • feature engineering
  • model lifecycle management
  • APIs/microservices
  • CI/CD
  • MLOps
  • LLMOps
  • Vertex AI Pipelines
  • Cloud Build
  • GKE
  • Cloud Run
  • Pub/Sub
  • BigQuery
  • Cloud SQL/Spanner
  • Memorystore
  • Terraform
  • application design patterns
  • agentic design patterns
  • security for AI/ML systems
  • data privacy
  • model poisoning
  • adversarial attacks
  • enterprise architecture
  • Cloud Native principles
  • hyperscaler platform experience (AWS, Azure, GCP)
  • containers (Docker, Kubernetes)
  • serverless functions
  • managed databases
  • GenAI tools

Nice to have

  • 8+ years experience as Software or Solution Architect
  • 5+ years hands-on with Google Cloud
  • 2+ end-to-end enterprise implementations in production
  • 4+ years designing and implementing Google Cloud networks, security controls, and landing zones

What the JD emphasized

  • enterprise AI platforms
  • LLM solutions
  • RAG and agentic solutions
  • end-to-end architectures
  • cloud-native development
  • security and governance for AI/ML systems
  • production environments
  • enterprise implementations

Other signals

  • architect and deliver enterprise AI platforms
  • optimize for scalability, reliability, security, and cost
  • design, fine-tune, evaluate, and govern LLM solutions
  • implement deployment, inference optimization, and monitoring
  • build RAG and agentic solutions
  • define end-to-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps
  • lead cloud-native development
  • implement security and governance for AI/ML systems