AI Site Reliability Engineer

What the role needs

• Cognizant is seeking an AI Site Reliability Engineer to build and operate the reliable, scalable, and secure infrastructure behind our AI and machine-learning platforms.
• Design, operate, and improve highly available infrastructure for AI training, inference, and data-processing workloads.
• Define and manage service-level objectives, reliability metrics, error budgets, and capacity plans.
• Build monitoring, logging, tracing, alerting, and AI-specific observability for model-serving platforms.
• Lead incident response, root-cause analysis, post-incident reviews, and reliability improvements.
• Automate deployment, scaling, rollback, disaster recovery, and routine operational tasks.
• Operate Kubernetes-based services, cloud platforms, GPU clusters, networking, storage, and CI/CD systems.
• Improve model-serving performance, including latency, throughput, GPU utilization, availability, and cost efficiency.
• Develop safe automation and self-healing workflows for common production issues.
• Establish operational standards for model versioning, rollouts, canary releases, feature flags, and rollback procedures.
• Collaborate with engineering and ML teams to make AI systems production-ready.
• Maintain clear runbooks, architecture documentation, and operational procedures.

From the employer

Cognizant is seeking an AI Site Reliability Engineer to build and operate the reliable, scalable, and secure infrastructure behind our AI and machine-learning platforms. You will work across cloud infrastructure, Kubernetes, GPU compute, model-serving systems, observability, automation, and incident response. You will partner with software, data science, security, and platform teams to ensure our AI systems meet high standards for availability, latency, performance, cost, and operational safety. Responsibilities • Design, operate, and improve highly available infrastructure for AI training, inference, and data-processing workloads. • Define and manage service-level objectives, reliability metrics, error budgets, and capacity plans. • Build monitoring, logging, tracing, alerting, and AI-specific observability for model-serving platforms. • Lead incident response, root-cause analysis, post-incident reviews, and reliability improvements. • Automate deployment, scaling, rollback, disaster recovery, and routine operational tasks. • Operate Kubernetes-based services, cloud platforms, GPU clusters, networking, storage, and CI/CD systems. • Improve model-serving performance, including latency, throughput, GPU utilization, availability, and cost efficiency. • Develop safe automation and self-healing workflows for common production issues. • Establish operational standards for model versioning, rollouts, canary releases, feature flags, and rollback procedures. • Collaborate with engineering and ML teams to make AI systems production-ready. • Maintain clear runbooks, architecture documentation, and operational procedures. • Participate in an on-call rotation and help build a sustainable, low-noise operational culture. Required Qualifications • 4+ years of experience in SRE, infrastructure engineering, DevOps, platform engineering, or production engineering. • Strong Linux, networking, cloud, and distributed-systems experience. • Hands-on experience with Kubernetes and infrastructure-as-code tools such as Terraform. • Experience with at least one major cloud platform: AWS, Azure, or Google Cloud. • Proficiency in Python, Go, or another programming language used for automation and production systems. • Experience with observability platforms, metrics, logs, traces, and incident-management practices. • Experience operating production services with defined availability and performance targets. • Strong troubleshooting, communication, documentation, and systems-thinking skills. Preferred Qualifications • Experience operating machine-learning or generative-AI workloads in production. • Familiarity with GPUs, CUDA, model-serving systems, vector databases, or high-performance computing. • Experience with tools such as Prometheus, Grafana, OpenTelemetry, Argo CD, Helm, or service meshes. • Knowledge of MLOps practices, model lifecycle management, and ML/data pipelines. • Experience with reliability testing, chaos engineering, load testing, or capacity modeling. • Understanding of AI security, data privacy, model-safety controls, and access management. Success in This Role Within the first year, you will help reduce critical incidents, improve detection and recovery times, increase platform availability, improve infrastructure utilization, and create repeatable operational standards for deploying and running AI systems at scale. The salary range for this role is between $142,000 and $167,500 depending on skills and qualifications of the candidate. Applications will be accepted till 10/31/2026 Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws. Applicants for US based positions must be currently authorized to work for any employer in the United States. The company is unable to provide employment-based immigration sponsorship for US based positions. If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email CareersNA2@cognizant.com for roles based in the Americas or CareersIndia2@cognizant.com for roles based in India. About Cognizant: Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.