New opportunity

AI Platform Lead

Sensata Technologies · Pune, India

About this role

The AI Platform Lead will spearhead the development and evolution of Sensata's enterprise AI platform, providing the shared services, tools, and infrastructure necessary for scalable, secure, and reusable AI solutions. This role acts as a bridge between AI architecture and solution delivery, ensuring robust governance and engineering foundations. Responsibilities: Own, build, and manage the enterprise AI platform on Azure, including Azure AI Foundry, Azure OpenAI, LLM gateways, and telemetry. Implement reference architectures, manage Dev/Test/Prod environments, and oversee CI/CD pipelines and infrastructure-as-code. Establish MLOps/LLMOps practices for model deployment, versioning, and performance monitoring. Ensure platform security, identity management, and responsible AI guardrails. Build reusable components, APIs, and automation to accelerate AI development. Lead and mentor platform engineers, drive platform optimization, and manage the roadmap. Qualifications: 8+ years of experience in software engineering, platform engineering, or cloud infrastructure. Strong knowledge of cloud platforms, system integration, and modern software development. Experience supporting AI, machine learning, or generative AI solutions in enterprise environments. Proficiency in platform governance, security controls, and operational excellence. Proven ability to lead complex technical initiatives and influence cross-functional teams. Advanced degree in Computer Science, Engineering, or related field preferred.

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

Skills for this role

AzureAzure AI FoundryAzure OpenAILLM gatewayMLOpsLLMOpsCI/CDInfrastructure-as-CodeCloud ArchitectureSystem IntegrationGenerative AIMachine LearningPlatform EngineeringAPI DevelopmentTelemetry

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 Ownership
The primary mandate is to build and manage the end-to-end AI platform on Azure. This requires deep technical expertise in Azure AI Foundry, Azure OpenAI, and LLM gateways to provide a stable, scalable foundation for internal teams.
Operationalizing MLOps and LLMOps
You must establish rigorous practices for model deployment, versioning, and performance monitoring. The goal is to move AI solutions from pilot to production reliably, ensuring consistent release management and incident response.
Governance and Security Guardrails
Implementing robust identity management, access controls, and responsible AI guardrails is critical. You are responsible for ensuring that all platform components meet enterprise security standards and compliance requirements.
Developer Enablement and Automation
A key priority is building reusable components, APIs, and infrastructure-as-code templates. This accelerates the work of data scientists and AI engineers by standardizing development environments and CI/CD pipelines.

Likely challenges to think through

Balancing Innovation with Standardization
You will face the tension between providing flexible tools for rapid AI experimentation and enforcing the strict governance and architectural standards required for enterprise production. Prepare to discuss how you negotiate these trade-offs with cross-functional stakeholders.
Scaling AI Across Global Teams
Coordinating delivery across global teams while maintaining platform reliability and cost visibility is a significant operational hurdle. Be ready to explain how you manage technical dependencies and influence teams without direct authority.
Managing Rapidly Evolving AI Technologies
The AI landscape shifts quickly, particularly with LLM integrations. You must demonstrate how you evaluate new technologies for the roadmap while ensuring the current platform remains stable and performant for existing users.

Practice your answers

Technical practice

Designing an Enterprise LLM Gateway

What to coverExplain the architectural considerations for building an LLM gateway on Azure. Focus on how you would handle rate limiting, request logging, security, and cost tracking across multiple AI models.

Implementing MLOps and LLMOps Pipelines

What to coverDescribe your approach to CI/CD for AI solutions. Discuss how you manage model versioning, automated testing for model performance, and the transition from development to production environments.

Infrastructure-as-Code for AI Platforms

What to coverHow do you ensure consistency across Dev, Test, and Prod environments using Infrastructure-as-Code? Discuss your experience with tools like Terraform or Bicep in the context of Azure AI Foundry.

Ensuring Responsible AI Guardrails

What to coverWhat technical mechanisms would you implement to enforce responsible AI standards? Address how you would integrate content filtering, PII redaction, and safety monitoring within an enterprise platform.

Platform Telemetry and Monitoring

What to coverDetail your strategy for monitoring an enterprise AI platform. Explain how you track usage, latency, and reliability to provide actionable insights for optimization and cost management.

Scaling AI Solutions from Pilot to Production

What to coverDescribe the challenges of moving a model from a pilot phase to a production-ready service. Focus on operational readiness, incident response, and maintaining performance at scale.

Identity and Access Management in AI

What to coverHow do you manage secure access to AI services and data in a multi-tenant enterprise environment? Discuss the integration of Azure identity controls with AI platform components.

Building Reusable AI Components

What to coverHow do you design and document reusable APIs or templates to accelerate development for data scientists? Provide an example of how you balance flexibility for users with platform standardization.

Behavioural practice

Influencing Without Direct Authority

What to coverDescribe a time you had to influence cross-functional teams to adopt a new technical standard or platform architecture. How did you build consensus and manage resistance?

Managing Competing Priorities

What to coverExplain a situation where you had to balance multiple high-priority technical initiatives. How did you manage your time, assess risks, and ensure project milestones were met?

Mentoring and Team Development

What to coverProvide an example of how you have mentored platform engineers or junior team members. What was your approach to setting engineering standards and fostering professional growth?

Handling Ambiguous Technical Requirements

What to coverDescribe a project where the requirements were initially unclear. How did you define the scope, select the appropriate technologies, and ensure the final solution met business needs?

Sharpen your knowledge

Topics to prepare

Enterprise LLMOps Lifecycle
Focus on the end-to-end management of generative AI models, specifically model deployment, versioning, evaluation, and performance monitoring. Exercise: Draft a conceptual framework for transitioning an LLM application from a pilot phase to a production-ready state, including specific metrics for drift detection and quality assurance.
AI Platform Governance and Security
Understand the implementation of identity management, access controls, and responsible AI guardrails within a cloud-based AI ecosystem. Exercise: Outline a security strategy for an LLM gateway that ensures data privacy and prevents prompt injection, aligning with enterprise-grade compliance standards.

Skills to revise

Azure AI Foundry and OpenAI Integration
Gain hands-on familiarity with deploying and managing services within the Azure AI ecosystem. Exercise: Create a mock architecture diagram showing how Azure AI Foundry connects to Azure OpenAI services, incorporating an LLM gateway for centralized request management and telemetry logging.
Infrastructure-as-Code (IaC) for AI
Develop proficiency in automating the provisioning of AI environments to ensure consistency across Dev, Test, and Prod. Exercise: Write a sample Terraform or Bicep script to provision a scalable Azure environment that includes an API endpoint and a monitoring component for tracking AI service usage.
CI/CD Pipeline Design for AI
Build automated pipelines that integrate model testing and deployment. Exercise: Design a CI/CD workflow that triggers automated regression tests for a generative AI model whenever a new version is pushed to the repository, ensuring performance benchmarks are met before deployment.

Your preparation checklist

Architect an Azure-based AI Platform Strategy
Prepare a high-level design document outlining how you would integrate Azure AI Foundry, Azure OpenAI, and an LLM gateway to support enterprise-scale AI. Focus on how you would manage Dev/Test/Prod environments using Infrastructure-as-Code (IaC) and CI/CD pipelines to ensure consistent deployments.
Develop an MLOps/LLMOps Framework
Create a brief presentation or document detailing your approach to model lifecycle management, including versioning, automated evaluation, and performance monitoring. Explain how you would implement responsible AI guardrails and security controls within this framework.
Prepare Case Studies on Platform Engineering
Select two past projects where you built reusable components, APIs, or developer tools that accelerated team productivity. Be ready to discuss how you measured the success of these initiatives, specifically regarding platform reliability, cost optimization, and developer experience.
Formulate Strategic Questions for the Interview
Prepare thoughtful questions regarding the current maturity of Sensata's AI infrastructure, the primary bottlenecks faced by their data science teams, and how the platform roadmap aligns with the company's broader industrial technology goals.

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