22 hours ago
San Jose, CA, USAMid Level
H1B sponsor
Base Salary
$142k - $168k/yr
Responsibilities
- Design, operate, and improve highly available infrastructure for AI training, inference, and data-processing workloads.
- Define 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 production systems.
- Improve model-serving latency, throughput, GPU utilization, availability, and cost efficiency.
- Develop safe automation and self-healing workflows for common production issues.
- Establish 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 runbooks, architecture documentation, and operational procedures.
- Participate in an on-call rotation and help build a sustainable operational culture.
Requirements
- At least 4 years of experience in SRE, infrastructure engineering, DevOps, platform engineering, or production engineering.
- Strong experience with Linux, networking, cloud platforms, and distributed systems.
- 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 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 Prometheus, Grafana, OpenTelemetry, Argo CD, Helm, or service meshes.
- Knowledge of MLOps, model lifecycle management, and ML or 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.
Benefits
- Annual salary range of $142,000 to $167,500 depending on skills and qualifications.
- Applications will be accepted until 10/31/2026.
- Participation in an on-call rotation is required.
Categories
DevOpsSite Reliability
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.