Position Title: AI Platform Architect (Cloud)
Location: Milpitas, CA
Experience
12+ Years in Cloud, Platform Engineering, Infrastructure, and Enterprise Architecture
5+ Years in AI/ML, Data Platforms, or Cloud-Native Architecture
3+ Years designing Enterprise AI/GenAI Platforms
Job Summary
We are seeking an experienced AI Platform Architect (Cloud) to lead the design, implementation, and evolution of enterprise-scale AI platforms across public cloud environments such as Azure, AWS, and Google Cloud Platform (Google Cloud Platform).
The ideal candidate will architect scalable, secure, and cost-optimized AI ecosystems that support Generative AI, Large Language Models (LLMs), Machine Learning, MLOps, AI Agents, and Data Science workloads. This role requires expertise in AI infrastructure, cloud-native platforms, platform engineering, governance, security, and operational excellence.
The AI Platform Architect will collaborate with data scientists, AI engineers, cloud teams, security teams, and business stakeholders to deliver enterprise AI capabilities that accelerate innovation while ensuring governance, compliance, and reliability.
Key Responsibilities
AI Platform Strategy & Architecture
- Define enterprise AI platform architecture and roadmap across cloud environments.
- Design scalable and secure AI platforms supporting GenAI, AI Agents, LLMs, RAG solutions, and traditional ML workloads.
- Develop AI reference architectures, technology standards, and governance frameworks.
- Establish reusable AI platform services enabling self-service AI capabilities.
- Drive AI platform modernization and cloud transformation initiatives.
Cloud Architecture & Engineering
- Design cloud-native AI infrastructure leveraging Azure, AWS, or Google Cloud Platform services.
- Architect multi-region and highly available AI platforms.
- Define cloud landing zones, networking, security, and operational guardrails.
- Design scalable compute environments utilizing GPUs, Kubernetes, and serverless technologies.
- Establish platform reliability, resiliency, and disaster recovery strategies.
Generative AI & LLM Platforms
- Architect enterprise GenAI solutions using OpenAI, Azure OpenAI, Anthropic, Gemini, Llama, and other foundation models.
- Define architecture for Retrieval-Augmented Generation (RAG) implementations.
- Design vector database integration and knowledge management frameworks.
- Establish AI model serving and inferencing platforms.
- Support AI Agent and autonomous workflow architectures.
MLOps & AI Operations
- Design end-to-end MLOps frameworks supporting model development, deployment, monitoring, retraining, and governance.
- Implement CI/CD and model lifecycle automation.
- Enable model registry, experimentation, feature management, and deployment pipelines.
- Establish AI observability, monitoring, explainability, and model performance tracking.
- Drive AI operational excellence and reliability practices.
Data & AI Integration
- Define architectures integrating AI platforms with enterprise data ecosystems.
- Support structured, semi-structured, and unstructured data processing.
- Design data pipelines supporting AI training and inferencing workloads.
- Establish data governance, lineage, and AI-ready data capabilities.
- Enable enterprise knowledge management for GenAI use cases.
Security, Risk & Governance
- Design secure AI architectures aligned with enterprise security standards.
- Implement AI governance, compliance, privacy, and responsible AI frameworks.
- Establish security controls for AI applications, APIs, models, and data.
- Define identity and access management frameworks.
- Manage risks associated with AI adoption and model usage.
Platform Engineering & Automation
- Implement Infrastructure as Code (IaC) and GitOps practices.
- Design platform engineering solutions enabling developers and data scientist self-service.
- Automate environment provisioning, deployment, scaling, and lifecycle management.
- Establish standardized reusable platform patterns.
- Drive DevSecOps and AI Platform automation initiatives.
Leadership & Stakeholder Management
- Act as the technical authority for cloud AI platform architecture.
- Mentor AI Engineers, Platform Engineers, Cloud Architects, and Data Scientists.
- Develop executive presentations, architecture roadmaps, and business cases.
- Collaborate with customers, leadership teams, governance boards, and technology partners.
- Support innovation workshops and AI transformation programs.
Required Technical Skills
Cloud Platforms
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (Google Cloud Platform)
- Hybrid & Multi-Cloud Architecture
AI & Generative AI
- Azure OpenAI
- OpenAI APIs
- Anthropic Claude
- Google Gemini
- Meta Llama
- Hugging Face
- LangChain
- LangGraph
- Semantic Kernel
- AutoGen
- AI Agents Frameworks
MLOps & AI Operations
- MLflow
- Kubeflow
- SageMaker
- Vertex AI
- Azure Machine Learning
- Model Registries
- Feature Stores
- Experiment Tracking
- AI Monitoring Platforms
Data Platforms
- Databricks
- Snowflake
- BigQuery
- Synapse Analytics
- Data Lake Architectures
- Streaming Platforms
- Data Pipelines
Vector Databases & RAG
- Pinecone
- Weaviate
- Chroma
- Azure AI Search
- Elasticsearch
- OpenSearch
- Knowledge Graphs
Container & Platform Technologies
- Kubernetes
- AKS
- EKS
- GKE
- Docker
- OpenShift
- Service Mesh
- API Gateways
Automation & DevOps
- Terraform
- Bicep
- CloudFormation
- GitHub Actions
- Azure DevOps
- Jenkins
- ArgoCD
- CI/CD Pipelines
Security & Governance
- Zero Trust Architecture
- IAM
- Secrets Management
- Policy Enforcement
- Responsible AI Governance
- Compliance Frameworks
Observability & Reliability
- Prometheus
- Grafana
- OpenTelemetry
- ELK Stack
- Dynatrace
- New Relic
- Datadog
Required Qualifications
- Bachelor’s or master’s degree in computer science, Engineering, Data Science, Information Technology, or related field.
- 12+ years of experience in enterprise IT, cloud architecture, platform engineering, or infrastructure.
- 5+ years designing AI/ML, data, or analytics platforms.
- Experience architecting cloud-native solutions at enterprise scale.
- Strong understanding of AI/ML lifecycle management and MLOps principles.
- Hands-on experience with cloud-native and Kubernetes-based architecture.
- Experience leading large-scale transformation programs.
- Excellent communication, executive presentation, and stakeholder-management skills.
Preferred Certifications
Cloud Certifications
- Microsoft Certified: Azure Solutions Architect Expert
- AWS Certified Solutions Architect Professional
- Google Professional Cloud Architect
AI Certifications
- Microsoft Azure AI Engineer Associate
- AWS Machine Learning Specialty
- Google Professional Machine Learning Engineer
Kubernetes & Platform Certifications
- Certified Kubernetes Administrator (CKA)
- Certified Kubernetes Security Specialist (CKS)
- HashiCorp Terraform Certification
Preferred Experience
- Enterprise GenAI platform implementations.
- AI Factory and AI Center of Excellence initiatives.
- Large-scale LLM deployments.
- AI Agent frameworks and autonomous workflow solutions.
- Financial Services, Healthcare, Manufacturing, Telecom, Retail, or Technology sectors.
- Multi-cloud AI architecture and governance programs.
- AI/ML platform modernization initiatives.
- Responsible AI and enterprise AI governance programs.
Key Competencies
Technical Leadership
- AI Platform Architecture
- Cloud Architecture
- Enterprise Solution Design
- Technology Strategy
Platform Engineering
- MLOps
- AI Operations
- DevSecOps
- Automation First Approach
Business Leadership
- Executive Stakeholder Management
- Customer Engagement
- Strategic Roadmap Development
- Innovation Leadership
Governance & Risk
- Responsible AI
- Compliance & Security
- AI Risk Management
- Regulatory Alignment
Success Measures
- Successful adoption of enterprise AI platform capabilities.
- Accelerated deployment of AI and GenAI use cases.
- Reduced AI model deployment timelines through automation.
- Improved developer and data scientist productivity.
- Secure and compliant AI operation across cloud environments.
- Enhanced platform reliability, scalability, and resilience.
- Increased reuse of AI platform services and components.
- Measurable business value from AI transformation initiatives.
