New opportunity

Senior Platform and Machine Learning Operations Engineer

Sensata Technologies · Pune, India

About this role

The Senior Platform and Machine Learning Operations Engineer designs, builds, and enhances the enterprise AI platform that enables the development, deployment, and operation of AI solutions at scale. This role focuses on delivering reusable platform capabilities, automation, integrations, and engineering tools that accelerate AI solution delivery while ensuring security, reliability, and operational excellence.

Responsibilities

Build and run core components of Sensata's Azure AI platform, including Azure AI Foundry, Azure OpenAI, the LLM gateway, telemetry and shared developer tools, under the direction of the AI Platform Lead.
Build and maintain Dev/Test/Prod environments, CI/CD pipelines and infrastructure-as-code (Bicep/Terraform) so AI solutions are built, tested and released the same way every time.
Implement MLOps/LLMOps pipelines for model and prompt deployment, evaluation, versioning, drift and performance monitoring, and rollback.
Run production operations for platform services, including release management, observability, alerting and incident response.
Implement identity and access controls, secrets management and responsible AI guardrails, working with IT on the underlying cloud, network and identity foundations.
Build reusable templates, SDKs, APIs and automation that help AI engineers and data scientists move from prototype to production quickly.
Track and optimize platform usage, performance, reliability and cost.
Design, develop, test, deploy, and maintain software for defined products or systems, writing code and building reusable modules.

Required qualifications

5+ years of experience in software, platform, cloud or DevOps engineering, including hands-on work running production systems.
Professional proficiency in spoken and written English.
Hands-on experience deploying and operating ML or GenAI models in production.
Strong Python skills (C# or Java a plus).
Strong Azure experience, with Azure AI Foundry, Azure OpenAI, Entra ID and API Management preferred.
Hands-on experience with CI/CD (Azure DevOps or GitHub Actions), infrastructure-as-code (Bicep/Terraform) and containers (Docker, AKS or Azure Container Apps).
Experience with MLOps/LLMOps tools and practices for model deployment, evaluation, versioning and monitoring.
Working knowledge of identity and access management, secrets management, monitoring and logging.
Strong troubleshooting skills and a track record of keeping production systems reliable.
Bachelor's degree in Computer Science, Software Engineering, Information Systems or a related technical field.

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

Skills for this role

PythonC#JavaAzureAzure AI FoundryAzure OpenAIEntra IDAPI ManagementAzure DevOpsGitHub ActionsBicepTerraformDockerAKSAzure Container AppsMLOpsLLMOps

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 Infrastructure Development
Design, build, and maintain core components of Sensata's Azure AI platform, including Azure AI Foundry, Azure OpenAI, and LLM gateways using infrastructure-as-code tools like Bicep and Terraform to ensure reliable Dev/Test/Prod environments.
MLOps and LLMOps Pipeline Implementation
Implement robust MLOps and LLMOps pipelines for model and prompt deployment, automated evaluation, versioning, drift and performance monitoring, and reliable rollback capabilities.
Platform Reusability and Developer Acceleration
Build reusable templates, SDKs, APIs, and automation tools that empower AI engineers and data scientists to transition from prototypes to production efficiently.
Security, Governance, and Production Operations
Ensure operational excellence by implementing identity and access controls, secrets management, responsible AI guardrails, comprehensive telemetry, and proactive incident response.

Likely challenges to think through

Balancing Rapid AI Prototyping with Strict Enterprise Security
A key challenge lies in accelerating developer workflows through reusable templates while simultaneously enforcing robust identity controls, secrets management, and responsible AI guardrails. To prepare, review best practices for integrating security scans and compliance checks directly into CI/CD pipelines without slowing down iteration cycles.
Managing Complex LLMOps and Model Drift in Production
Maintaining reliable production operations for generative AI models involves unique difficulties around prompt versioning, evaluation, and detecting data or drift performance degradation. Preparation should focus on designing comprehensive observability and telemetry frameworks tailored specifically to large language model outputs and gateway traffic.

Practice your answers

Technical practice

Designing Azure AI Platform Architecture

What to coverDiscuss how you would design and run core components of an enterprise Azure AI platform, specifically integrating services like Azure AI Foundry, Azure OpenAI, and an LLM gateway. Candidates should address network topologies, scalability, telemetry collection, and how to provide shared developer tools under leadership guidance.

Infrastructure-as-Code with Terraform or Bicep

What to coverExplain your approach to building and maintaining consistent Dev/Test/Prod environments using infrastructure-as-code tools such as Bicep or Terraform. Candidates should focus on modular design, state management, version control, and ensuring repeatable automated releases across environments.

MLOps and LLMOps Pipelines

What to coverDescribe how you implement MLOps and LLMOps pipelines for model and prompt deployment, evaluation, versioning, drift monitoring, and rollback. Guidance should cover automated evaluation metrics, handling model drift, and executing safe rollbacks when performance degrades in production.

CI/CD and Containerization Strategies

What to coverHow do you configure CI/CD pipelines (using Azure DevOps or GitHub Actions) alongside container platforms (Docker, AKS, or Azure Container Apps)? Candidates should discuss build automation, security scanning, image registry management, and zero-downtime deployment strategies for containerized AI services.

Identity, Access Control, and Secrets Management

What to coverExplain how you implement identity and access controls (using Entra ID), API management, and secure secrets management within cloud platforms. Discuss principle of least privilege, secure credential rotation, and integrating these controls with underlying cloud foundations.

Observability, Telemetry, and Incident Response

What to coverDetail your strategy for running production operations, including monitoring, observability, logging, and alerting for platform services. Candidates should address handling sudden latency spikes or API failures, setting up meaningful SLIs/SLOs, and structured incident response protocols.

Accelerating Prototype to Production for AI Engineers

What to coverHow would you build reusable templates, SDKs, APIs, and automation tools to help data scientists and AI engineers transition prototypes into production rapidly? Focus on developer experience, abstraction layers, and maintaining operational standards without creating friction.

Tracking and Optimizing Platform Cost and Performance

What to coverDiscuss methods for tracking and optimizing platform usage, resource consumption, performance, and cost at enterprise scale. Candidates should mention cloud cost management tools, right-sizing container clusters, and optimizing token usage or compute instances for AI models.

Behavioural practice

Handling Competing Priorities in a Fast-Paced Environment

What to coverDescribe a situation where you had to manage multiple competing tasks and demands while building or maintaining platform infrastructure. Use the STAR method to explain how you prioritized workloads, communicated with stakeholders, and met critical delivery milestones.

Collaborating Across Functions on Troubleshooting Complex Issues

What to coverShare an example of collaborating with cross-functional teams (such as data scientists, IT, or external vendors) to resolve a complex production defect or performance bottleneck. Highlight your communication style, technical ownership, and how you ensured knowledge sharing.

Taking Ownership of Quality and Security Standards

What to coverTell me about a time when you identified a security risk, compliance gap, or reliability flaw in an existing release pipeline or production system. Explain the steps you took to take ownership, implement guardrails, and document the process for peers.

Adapting to New Technologies and Standardizing Practices

What to coverProvide an example where you evaluated a new cloud or AI technology and introduced it into an established engineering standard or architecture. Discuss how you evaluated the technology, managed risks and dependencies, and brought your team along with the decision.

Sharpen your knowledge

Topics to prepare

Enterprise AI Platform Architecture
Conceptual topic covering the design of scalable AI platforms, LLM gateways, telemetry collection, and shared developer tooling on cloud infrastructure.
MLOps and LLMOps Lifecycle Management
Conceptual topic focusing on model and prompt deployment strategies, automated evaluation, versioning, drift monitoring, and rollback mechanisms.
Cloud Security, IAM, and Governance
Conceptual topic examining identity and access controls, secrets management, compliance, and responsible AI guardrails within enterprise environments.

Skills to revise

Infrastructure-as-Code with Terraform and Bicep
Hands-on skill involving the creation and maintenance of declarative infrastructure for Dev/Test/Prod environments on Azure.
CI/CD Pipeline Automation
Hands-on skill focused on configuring build and release automation using Azure DevOps or GitHub Actions for reliable software delivery.
Containerization and Orchestration
Hands-on skill utilizing Docker, AKS, or Azure Container Apps to package and run production microservices and AI workloads.

Your preparation checklist

Review Core Azure AI and Cloud Platform Requirements
Completion looks like having a clear, articulated understanding of Azure AI Foundry, Azure OpenAI services, LLM gateways, and cloud foundations (such as Entra ID and API Management) to align with enterprise platform needs.
Prepare Infrastructure-as-Code and CI/CD Artifacts
Completion looks like assembling sample Bicep or Terraform templates along with Azure DevOps or GitHub Actions workflow configurations that demonstrate repeatable environment provisioning and automated testing.
Design an MLOps and LLMOps Pipeline Walkthrough
Completion looks like creating a documented architecture or diagram outlining model deployment, prompt versioning, automated evaluation, performance drift monitoring, and rollback strategies.
Audit Containerization and Orchestration Expertise
Completion looks like reviewing hands-on experience with Docker, Azure Kubernetes Service (AKS), or Azure Container Apps, ensuring you can explain container packaging and production scaling.
Formulate Security, IAM, and Guardrail Strategies
Completion looks like preparing talking points and examples around implementing identity and access controls, secrets management, and responsible AI guardrails within cloud environments.
Draft Technical and Scenario-Based Interview Evidence
Completion looks like organizing past professional experiences using the STAR method, specifically highlighting production reliability track records, troubleshooting complex defects, and Python development skills.
Prepare Thoughtful Questions for the Employer
Completion looks like drafting targeted questions regarding Sensata's AI platform roadmap, collaboration between AI engineers and data scientists, and tooling standards under the AI Platform Lead.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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