Site Reliability Engineer

Who We AreAt Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the world's leading businesses.
We are at the heart of progress; with proven expertise and a continuous flow of AI-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge.
For our people—Kyndryls—that means doing purposeful work that powers human progress.
Join us and experience a flexible, supportive environment where your well-being is prioritized and your potential can thrive.
The RoleWe are looking for a Site Reliability Engineer to ensure the reliability, performance, and observability of our enterprise AI platform.
This role monitors and supports production systems running on Kubernetes and cloud services (currently Azure), and extends traditional observability to cover how AI solutions behave and how models are used.
Working closely with DevOps engineers, the SRE will help automate and harden the CI/CD pipelines that developers rely on to deliver applications.
The ideal candidate is an organized, experienced cloud operations professional with strong reliability engineering fundamentals who is committed to defining service level objectives, reducing operational toil, and driving continuous improvement through data and automation.
Prior experience with AI is not required; we value core SRE skills and the ability to learn new technologies quickly.

Key Responsibilities
Monitor platform health: Monitor and support running platform systems, defining and tracking SLIs, SLOs, and error budgets for availability, latency, and throughput across the platform.
Build observability for AI workloads: Design and maintain dashboards, alerts, and telemetry that cover AI solution behavior and model usage, such as token consumption, request latency, throttling and quota limits, error rates, cost attribution by application and team, and output quality or drift signals.
Operate Kubernetes and cloud services: Run and support Kubernetes clusters and the supporting cloud services in our Azure environment, including API management, managed model services, and container orchestration, covering capacity planning, autoscaling, upgrades, workload health, and resource optimization.
Automate CI/CD with DevOps: Partner with DevOps engineers to automate, standardize, and improve CI/CD pipelines in GitHub Actions, embedding reliability checks, automated testing, progressive delivery, and rollback practices that enable developers to ship safely and quickly.
Automate infrastructure: Use Terraform and other infrastructure-as-code tools to provision and manage cloud environments in a repeatable, secure, and compliant manner.
Respond to and prevent incidents: Participate in on-call and incident response, lead blameless post-incident reviews, maintain runbooks, and drive corrective actions that reduce recurrence and manual toil.
Plan capacity and resilience: Conduct capacity planning, load and failure testing, and disaster recovery readiness for platform components.
Collaborate and document: Work with developers, data scientists, security, and enterprise architecture teams to align reliability practices with platform standards, and document operational guidance for platform consumers.
Who You AreRequired

Qualifications:
Bachelor's degree in Computer Science, Engineering, Info Systems, or related field.5+ years of practical experience in site reliability engineering, DevOps, or cloud operations.
Strong hands-on experience running production workloads on Kubernetes, including deployment and configuration management, networking, autoscaling, cluster upgrades, and troubleshooting.
Experience operating production workloads in a major public cloud (Azure, AWS, or GCP).
Azure experience is helpful but not required; strong cloud operations skills are expected to transfer.
Proficiency with observability tooling such as Azure Monitor, Prometheus, Grafana, or Dynatrace, including OpenTelemetry-based tracing, metrics, and logging.
Experience defining SLIs, SLOs, and error budgets, and building actionable alerting that reduces noise.
Experience with automation across the delivery lifecycle, including CI/CD pipelines (preferably GitHub Actions), infrastructure as code (preferably Terraform), and scripting or programming (e.g., Python, Bash, PowerShell, or Go), and the ability to collaborate with DevOps engineers on pipeline design and reliability.
Strong incident management, troubleshooting, and communication skills, with experience in on-call rotations and blameless post-incident reviews.
Curiosity and willingness to learn how AI solutions and models behave in production.
No prior AI or machine learning experience is required.
Preferred

Qualifications:
Experience with Azure services such as Azure Kubernetes Service, Azure API Management, or Azure AI Foundry.
Experience monitoring AI/ML or generative AI workloads, including model quality, drift, safety, and cost metrics.
Experience with Kubernetes ecosystem to