New opportunity

Senior AI Operations Engineer

Allegion · Bangalore, India

About this role

The Senior AI Operations Engineer will be responsible for operating and maintaining enterprise AI platforms and services across development, test, and production environments. This individual contributor role focuses on ensuring platform availability, performance, and scalability while supporting AI workloads including copilots, chatbots, and RAG services. Key responsibilities include: AI Platform Operations: Managing Azure AI services, application hosting, and model access layers across multiple providers. Deployment & Reliability: Operationalizing AI workloads, managing CI/CD pipelines, and implementing monitoring and observability using Azure Monitor and OpenTelemetry. Infrastructure & Automation: Developing Infrastructure as Code (Terraform) and establishing self-service capabilities for engineering teams. Security & Governance: Implementing secure cloud configurations, identity management, and ensuring compliance with responsible AI practices. Required Qualifications: Bachelor's degree in a STEM discipline or equivalent experience. Minimum 8 years of experience in Cloud Platform Engineering, DevOps, SRE, or AI Operations. Hands-on experience with Microsoft Azure, containerized workloads (Kubernetes/Docker), and production AI/ML platforms. Demonstrated experience owning shared infrastructure and delivery pipelines. Preferred Qualifications: Experience with multi-provider model access (AWS Bedrock, Google Vertex AI), GitOps tooling (Argo CD/Flux), and advanced Azure API management. The role is based in Bangalore and requires standard IST working hours with occasional after-hours support for critical incidents.

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

Skills for this role

Microsoft AzureAzure OpenAIAzure AI FoundryAzure Machine LearningAzure Kubernetes ServiceDockerTerraformAzure DevOpsGitHub ActionsPythonBashPowerShellLinuxInfrastructure as CodeCI/CDKubernetesMonitoringObservabilityGenerative AIRAGAI Governance

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

AI Platform Reliability and Scalability
The role requires maintaining high availability and performance for enterprise AI services, including Azure OpenAI and RAG workloads. You must ensure that the underlying infrastructure, such as Azure Kubernetes Service and App Services, is configured for optimal scaling and stability to support production-grade AI applications.
Infrastructure as Code and Automation
You are expected to develop and maintain reusable Terraform modules to standardize environment provisioning across development, test, and production. This priority focuses on creating 'golden paths' and self-service capabilities that allow other engineering teams to deploy AI workloads with minimal manual intervention.
Governed Model Access and Security
Managing a secure model-access layer is critical, involving provider routing, failover, and strict identity management. You must implement secure cloud configurations, manage secrets via Azure Key Vault, and enforce responsible AI practices, including token monitoring and rate limiting.
CI/CD and Operational Observability
You must build robust CI/CD pipelines that incorporate automated model evaluations, content safety checks, and deployment validations. Additionally, implementing comprehensive observability using Azure Monitor and OpenTelemetry is essential for tracking AI-specific signals like model drift and token consumption.

Likely challenges to think through

Balancing Innovation with Strict Governance
The role demands rapid deployment of AI services while adhering to strict security and compliance standards. Prepare by demonstrating how you have implemented automated 'guardrails' in CI/CD pipelines that enforce security policies without stifling developer velocity.
Managing Multi-Provider AI Complexity
While the focus is on Azure, the role involves operating a gateway that may route across AWS Bedrock and Google Vertex AI. Prepare by discussing your experience with abstraction layers or gateway technologies like LiteLLM that normalize interactions across disparate AI providers.
Troubleshooting Non-Deterministic AI Workloads
Unlike traditional software, AI services can exhibit non-deterministic behavior, such as latency spikes or quality degradation. Prepare by explaining your approach to root cause analysis for AI-specific issues, such as distinguishing between infrastructure failures and model-level drift or grounding index freshness problems.

Practice your answers

Technical practice

Designing Multi-Provider AI Gateways

What to coverExplain how you would architect a unified model-access layer that routes requests between Azure OpenAI, AWS Bedrock, and Google Vertex AI. Discuss strategies for handling failover, rate limiting, and centralized logging of token consumption across these heterogeneous providers.

Operationalizing RAG Services

What to coverDescribe the key components of a production-ready Retrieval-Augmented Generation (RAG) pipeline. Focus on how you would monitor for grounding index freshness, data drift, and latency issues within an Azure-based environment.

Terraform for AI Infrastructure

What to coverHow do you manage state and modularity when provisioning AI platforms across development, test, and production? Suggest how to implement drift detection and ensure infrastructure compliance using Terraform in a CI/CD context.

Kubernetes for AI Workloads

What to coverWhat are the specific challenges of running containerized AI/ML workloads on Azure Kubernetes Service (AKS)? Address considerations for node pool configuration, GPU resource management, and implementing security policies for containerized AI services.

Implementing Observability for Generative AI

What to coverBeyond standard infrastructure metrics, what specific signals would you track to monitor the health of a Generative AI application? Discuss the use of OpenTelemetry and Azure Monitor to capture model quality, throttling events, and cost-per-request.

CI/CD for AI Models and Prompts

What to coverHow do you integrate model and prompt versioning into a standard CI/CD pipeline? Explain how you would implement automated gates for content safety, latency, and cost before promoting a new model version to production.

Securing AI Service Endpoints

What to coverDetail your approach to securing AI services using private networking and identity management. Explain how you would leverage Managed Identities, Azure Key Vault, and RBAC to ensure least-privilege access to sensitive AI models and data stores.

Troubleshooting 429 Throttling Events

What to coverIf an enterprise AI application is experiencing frequent 429 (Too Many Requests) errors, what steps would you take to diagnose the root cause? Discuss how you would analyze token consumption patterns and implement effective rate-limiting strategies.

Behavioural practice

Managing Conflicting Stakeholder Requirements

What to coverDescribe a situation where you had to balance the needs of Data Science teams (who want rapid experimentation) with Security/Governance teams (who require strict controls). How did you reach a compromise that maintained platform stability?

Handling Critical Production Incidents

What to coverTell me about a time you managed a high-priority production outage for a core platform service. How did you coordinate communication, perform root cause analysis, and ensure the issue did not recur?

Building Self-Service Capabilities

What to coverProvide an example of how you have built 'golden paths' or self-service infrastructure for other engineering teams. What was the impact on operational efficiency and risk reduction?

Ownership and Accountability

What to coverDescribe a time you identified a significant inefficiency or security risk in a shared platform you owned. What actions did you take to address it, and how did you ensure long-term accountability?

Sharpen your knowledge

Topics to prepare

AI Model Gateway and Governance
Review concepts of centralized model access layers, including provider routing, failover strategies, and token consumption monitoring. This is relevant to the role's requirement for managing multi-provider model access (Azure OpenAI, AWS Bedrock, Google Vertex AI) and implementing responsible AI practices.
Production AI/ML Lifecycle (MLOps)
Study the end-to-end operational lifecycle of generative AI, specifically RAG services, prompt versioning, and model evaluation gates. This is critical for the role's focus on operationalizing AI workloads and ensuring model quality and grounding index freshness.

Skills to revise

Infrastructure as Code (Terraform)
Practice creating reusable Terraform modules for Azure resources and implementing drift detection. This is essential for the role's requirement to automate platform provisioning and establish self-service capabilities for engineering teams.
Azure Kubernetes Service (AKS) Operations
Perform hands-on exercises in cluster configuration, including node pool management, ingress controllers, and autoscaling policies. This aligns with the requirement to support containerized AI workloads and build toward a production cluster platform.
CI/CD Pipeline Automation
Design pipelines in Azure DevOps or GitHub Actions that incorporate automated validation checks for model deployments and infrastructure changes. This is vital for the role's focus on governed, automated delivery and release engineering.
Observability and Monitoring
Configure Azure Monitor, Log Analytics, and OpenTelemetry to track AI-specific metrics like latency, token usage, and throttling events. This is necessary for the role's responsibility to ensure platform reliability and perform root cause analysis.

Your preparation checklist

Map your MLOps experience to Azure AI services
Prepare a summary of your experience managing Azure AI Foundry, Azure OpenAI, and Azure Machine Learning. Be ready to discuss how you have operationalized these services in production, specifically focusing on model versioning, deployment pipelines, and runtime configuration management.
Demonstrate Infrastructure as Code (IaC) expertise
Prepare a walkthrough of a complex Terraform project you led. Highlight how you created reusable modules, managed remote state, and implemented automated environment provisioning to support shared infrastructure consumed by other engineering teams.
Showcase container orchestration and reliability skills
Prepare to discuss your experience with Azure Kubernetes Service (AKS) or similar container platforms. Focus on how you have handled cluster health, autoscaling, networking, and security policies, and explain how you have used OpenTelemetry or Azure Monitor to ensure platform reliability.
Prepare for AI governance and security discussions
Review your experience implementing secure cloud configurations, such as private endpoints, managed identities, and RBAC. Be ready to explain how you have balanced developer self-service with responsible AI practices, compliance, and cost governance.
Formulate strategic questions for the interview
Prepare thoughtful questions regarding the current maturity of their AI platform, the specific challenges they face with model access and gateway operations, and how the team balances standard IST hours with the requirement for after-hours incident support.

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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