Position Title: Onshore AI Platform Architect
Location: Milpitas, CA (Onsite)
Employment: Full time
Experience: 12+ Yrs total/ 7+ Yrs Cloud, 5+ Yrs AI Platform
ROLE OVERVIEW:
We are seeking an Onshore AI Platform Architect to own the design, build, modernization, and governance of enterprise AI platform infrastructure across Azure and Google Cloud. The role establishes the scalable, secure, and cost-efficient platform layer—GPU compute, Kubernetes, MLOps/LLMOps, and Generative AI services—that enables data science, AI engineering, and business teams to build, deploy, and operate AI and GenAI solutions at scale.
KEY RESPONSIBILITIES:
▪ Define enterprise AI platform strategy, reference architectures, AI landing zones, and platform blueprints across Azure and Google Cloud Platform.
▪ Architect AI infrastructure: GPU clusters, Kubernetes (AKS/GKE), model training & inference platforms, vector databases, and feature stores.
▪ Design and operate Azure AI Foundry, Azure OpenAI, Azure ML, Google Vertex AI, and Gemini-based GenAI services.
▪ Establish enterprise MLOps and LLMOps frameworks—CI/CD pipelines, automated model training, deployment, evaluation, and rollback.
▪ Build GenAI patterns for RAG, agentic AI, and secure integration of enterprise data with LLMs (OpenAI, Vertex, Gemini, open-source models).
▪ Implement AI security and governance: identity (Entra ID / Google IAM), RBAC, private networking, Key Vault / Secret Manager, and Responsible AI controls.
▪ Automate platform provisioning via Terraform, Bicep, and GitOps; drive cost optimization and policy-driven governance.
▪ Design AI observability—model performance, drift, GPU utilization, and prompt/response monitoring using Azure Monitor and Cloud Operations.
▪ Advise executives and technical teams; lead architecture workshops, roadmaps, and mentor platform engineers.
REQUIRED TECHNICAL SKILLS:
AI Platforms & Cloud
▪ Azure AI Foundry, Azure OpenAI, Azure ML
▪ Google Vertex AI, Model Garden, Gemini
▪ Microsoft Azure + Google Cloud (multi-cloud)
MLOps / LLMOps & Automation
▪ Azure ML & Vertex AI Pipelines, MLflow, Kubeflow
▪ Terraform, Bicep, GitHub Actions, Azure DevOps
▪ Python, PowerShell, Bash, YAML
Containers & Infra
▪ Kubernetes, AKS, GKE, Docker, Registries
GPU compute, model serving, vector DBs, RAG
Security & Governance
▪ Entra ID, Google IAM, RBAC, Key Vault, Secret Manager
Defender for Cloud, Security Command Center, Responsible AI
REQUIRED QUALIFICATIONS:
▪ Bachelor’s degree in computer science, Engineering, IT, or related field.
▪ 12+ years of infrastructure/cloud/platform experience, including 7+ years in cloud architecture and 5+ years designing enterprise AI/ML platforms.
▪ Proven hands-on delivery of Azure AI and Google Vertex AI environments, including GPU and Kubernetes-based AI platforms.
▪ Strong grounding in networking, storage, security, and distributed systems; experience leading customer-facing architecture workshops.
PREFERRED CERTIFICATIONS & ENVIRONMENT
▪ Azure Solutions Architect Expert, Azure AI Engineer Associate; Google Professional Cloud Architect & Professional ML Engineer.
▪ Kubernetes (CKA/CKAD), TOGAF, and ITIL Foundation preferred.
▪ Preferred client environments: Semiconductor, Manufacturing, High-Tech, SaaS, and Enterprise IT running large-scale AI/GenAI across hybrid and multi-cloud.
“Tech Mahindra is an Equal Employment Opportunity employer. We promote and support a diverse workforce at all levels of the company. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin, or disability. All applicants will be evaluated solely on the basis of their ability, competence, and performance of the essential functions of their positions with or without reasonable accommodations. Reasonable accommodations also are available in the hiring process for applicants with disabilities. Candidates can request a reasonable accommodation by contacting the company ADA Coordinator at .”
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