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
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
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