New opportunity

AI Platform Architect

Siemens Energy Industrial Turbomachinery India Private Limited · Gurugram, India

About this role

Role Purpose: As an AI Platform Architect, you will design, evaluate, and lead the rollout of enterprise-scale AI platforms, including Generative AI, Agentic AI, orchestration layers, and shared AI services across a multi-cloud environment. You will define platform strategy, guide architecture decisions, and enable scalable adoption of AI solutions across business units.

Key Responsibilities:

Define and own enterprise AI platform architecture covering Generative AI, Agentic AI, orchestration layers, model serving, and shared AI services.
Design scalable, secure, and reusable AI platforms across AWS, Azure, and GCP.
Establish architectural standards for AI lifecycle management, observability, security, and governance.
Evaluate Agentic AI platforms, frameworks, and orchestration tools, including multi-agent systems.
Lead enterprise setup, onboarding, and rollout of Agentic AI platforms.
Architect and govern cloud-native AI infrastructure, including Kubernetes platforms (EKS, AKS, GKE) and GPU-based compute.
Implement CI/CD pipelines and Infrastructure-as-Code practices using tools such as Terraform and GitOps.
Ensure platform reliability, performance, cost efficiency, and alignment with security and compliance requirements.

Required Qualifications

8–10+ years of experience in AI platforms, cloud architecture, or platform engineering.
Proven expertise in multi-cloud architecture and platform scaling.
Hands-on experience with DevOps tools such as Terraform, GitHub/GitLab/Azure DevOps, Jenkins, Docker, Kubernetes, Helm, and GitOps.
Proficiency in programming and scripting languages including Python, Bash, PowerShell, and YAML.
Strong understanding of security compliance, AI governance, risk assessments, and operational controls.

Preferred Qualifications

Hands-on exposure to Agentic AI or orchestration frameworks.

Benefits

Remote working arrangements up to 2 days per week.
Opportunities to work with a global team on innovative projects.
Medical benefits, paid holidays, and parental leave.
Access to the Learn@Siemens-Energy platform for continual learning.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AIAgentic AIMulti-cloud ArchitectureAWSAzureGCPKubernetesEKSAKSGKETerraformGitOpsPythonBashPowerShellYAMLCI/CDInfrastructure-as-CodeAI GovernanceModel Serving

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enterprise AI Platform Strategy
The primary focus is to design and own the architecture for Generative and Agentic AI platforms across a multi-cloud environment (AWS, Azure, GCP). You must establish reusable patterns and standards that allow various business units to scale AI solutions from proof-of-concept to production while maintaining consistency.
Cloud-Native Infrastructure and Governance
You are responsible for architecting and governing Kubernetes-based infrastructure (EKS, AKS, GKE) and GPU compute resources. This includes implementing strict AI governance, data access controls, and auditability frameworks to ensure compliance and risk management across all AI deployments.
Operational Excellence and Automation
You must implement robust CI/CD pipelines and Infrastructure-as-Code (Terraform, GitOps) to manage the AI lifecycle. A key priority is ensuring platform reliability, performance, and cost efficiency through advanced observability and monitoring practices.

Likely challenges to think through

Balancing Multi-Cloud Complexity
Managing a unified AI platform across AWS, Azure, and GCP introduces significant architectural friction. Prepare by demonstrating how you abstract cloud-specific differences to create a seamless, vendor-agnostic developer experience while managing the overhead of maintaining multiple Kubernetes distributions.
Agentic AI Orchestration Maturity
Agentic AI and multi-agent systems are rapidly evolving. The challenge lies in evaluating and integrating these frameworks into an enterprise environment without compromising security or stability. Prepare by discussing how you evaluate emerging frameworks and define guardrails for autonomous agent workflows.
Standardization vs. Business Agility
As an architect, you must enforce strict governance and security standards without stifling the speed of business units. Prepare to explain your approach to creating 'golden paths' or self-service templates that encourage adoption of your platform standards by making them the easiest way for teams to build.

Practice your answers

Technical practice

Multi-Cloud AI Platform Strategy

What to coverExplain how you would design a unified AI platform architecture that maintains consistency across AWS, Azure, and GCP. Discuss your approach to abstracting cloud-specific services while ensuring portability for model serving and orchestration layers.

Agentic AI Orchestration

What to coverDescribe the architectural challenges of deploying multi-agent systems in an enterprise environment. Address how you would handle agent communication, state management, and the orchestration of workflows across disparate AI services.

Kubernetes and GPU Infrastructure

What to coverHow do you optimize Kubernetes clusters (EKS, AKS, GKE) for high-performance AI workloads requiring GPU acceleration? Discuss your strategies for managing GPU resource allocation, scheduling, and monitoring to ensure cost efficiency.

AI Governance and Security

What to coverWhat framework would you implement to ensure AI governance, including model auditability, data access controls, and risk management? Explain how you integrate these security requirements into the CI/CD pipeline.

Infrastructure-as-Code (IaC) and GitOps

What to coverDetail your experience using Terraform and GitOps for managing AI infrastructure. Explain how you ensure environment parity and drift detection when deploying complex AI platform components across multiple cloud regions.

Observability for AI Workflows

What to coverWhat metrics and logging strategies are essential for monitoring the health and performance of Generative AI and Agentic AI platforms? Discuss how you would implement observability to detect model drift or latency issues in production.

Scalable Model Serving

What to coverCompare different approaches to model serving at scale, such as serverless vs. containerized deployments. Discuss the trade-offs regarding latency, throughput, and cost when serving large language models in an enterprise setting.

CI/CD for AI Lifecycle Management

What to coverHow do you adapt traditional CI/CD pipelines to accommodate the unique requirements of the AI lifecycle, such as model versioning, data lineage, and automated retraining triggers?

Behavioural practice

Driving Architectural Standardization

What to coverDescribe a situation where you had to drive standardization across multiple business units with conflicting technical requirements. How did you balance the need for a unified platform strategy with the specific needs of individual teams?

Managing Stakeholder Expectations

What to coverTell me about a time you had to explain a complex AI architectural decision to non-technical stakeholders. How did you communicate the business value, risks, and long-term scalability of your proposed solution?

Handling Production Incidents

What to coverDescribe a challenging production issue you encountered in a cloud-native environment. What was your role in the resolution process, and what steps did you take to prevent recurrence through improved platform design?

Leading Cross-Functional Adoption

What to coverProvide an example of how you enabled the adoption of a new technology or platform across a large organization. What strategies did you use to overcome resistance and ensure successful onboarding for engineering teams?

Sharpen your knowledge

Topics to prepare

Agentic AI Orchestration
The role requires evaluating and rolling out Agentic AI platforms and multi-agent systems. Exercise: Review architectural patterns for multi-agent orchestration, focusing on how to manage state, inter-agent communication, and task delegation within a production environment. This is critical for the stated responsibility of defining reference architectures for agent-based workflows.
Enterprise AI Governance and Security
The position demands establishing standards for AI lifecycle management, risk assessment, and auditability. Exercise: Draft a framework for an AI governance policy that addresses model usage, data access controls, and compliance monitoring in a multi-cloud environment. This aligns with the requirement to ensure alignment with enterprise security and compliance requirements.
Multi-Cloud AI Infrastructure Strategy
The role involves designing scalable platforms across AWS, Azure, and GCP. Exercise: Compare the native AI and Kubernetes services (EKS, AKS, GKE) across these three providers, specifically focusing on GPU-based compute provisioning and cost-efficiency strategies. This is essential for the core responsibility of architecting cloud-native AI infrastructure.

Skills to revise

Infrastructure-as-Code (Terraform & GitOps)
The role requires implementing CI/CD and IaC practices. Exercise: Create a modular Terraform configuration that deploys a basic Kubernetes cluster and a model-serving endpoint. Integrate this with a GitOps workflow using tools like ArgoCD or Flux to demonstrate automated deployment. This directly supports the requirement for implementing CI/CD pipelines.
Kubernetes and GPU Resource Management
The role involves governing Kubernetes platforms and GPU-based compute. Exercise: Configure a Kubernetes manifest that requests specific GPU resources and implements resource quotas or limits to ensure cost efficiency. This is relevant to the requirement of architecting and governing cloud-native AI infrastructure.
Python Scripting for AI Automation
Proficiency in Python is required for platform engineering tasks. Exercise: Write a Python script that interacts with a cloud provider's API to monitor the health of an AI model-serving endpoint and triggers an alert if latency exceeds a defined threshold. This demonstrates the ability to implement observability and operational excellence.

Your preparation checklist

Develop a Multi-Cloud AI Reference Architecture
Create a high-level architectural diagram showcasing how you would deploy a unified AI platform across AWS, Azure, and GCP. Include components for model serving, GPU-based compute, and Kubernetes orchestration (EKS/AKS/GKE) to demonstrate your ability to handle multi-cloud complexity.
Prepare an Agentic AI Framework Evaluation
Draft a comparative analysis of current Agentic AI frameworks and orchestration tools. Be prepared to discuss how you would govern multi-agent systems, ensure secure model usage, and manage the lifecycle of AI agents within an enterprise environment.
Document an Infrastructure-as-Code (IaC) Strategy
Outline a GitOps-based workflow using Terraform and Kubernetes manifests to automate AI infrastructure provisioning. Explain how you would implement CI/CD pipelines to ensure consistent, secure, and reproducible deployments across different cloud environments.
Formulate an AI Governance and Security Framework
Prepare a set of standards for AI lifecycle management, focusing on data access controls, auditability, and risk assessment. Define how you would implement observability and monitoring to maintain platform reliability and compliance in a regulated industrial setting.
Draft Strategic Interview Questions
Prepare thoughtful questions for the hiring team, such as: How does the business currently prioritize AI use cases for scaling? What are the primary security or compliance challenges faced when integrating Agentic AI into existing industrial workflows? How is the platform team structured to support global business units?

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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