AI Site Reliability Engineer

Cognizant

Confirmed live today High trust

Quick summary

Work type
On-site
Location
San Jose, CA
Salary
$142,000–$167,500 / yr
Posted
5 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $180k
This role $155k
$132k $235k
below market most similar roles pay here above market

This role pays less than 69% of similar roles. Most pay $144,375–$215,000 — the blue band above. At the midpoint, this role pays about $155k versus about $180k for comparable roles.

Based on 240 similar postings.

Employer

About Cognizant

Cognizant is a multinational IT services and consulting company offering digital transformation, technology, and business process services. It serves clients across financial services, healthcare, manufacturing, and other industries worldwide.

Cognizant currently has 330 open roles on FindRole.

Listed pay typically runs $100,000–$135,000 across 211 roles with salary data.

Most-posted roles

View all roles at Cognizant

At a glance

TL;DR · AI Site Reliability Engineer

Role Posting Log in to save

The AI Site Reliability Engineer will join the team to build and operate reliable, scalable, and secure infrastructure for AI and machine-learning platforms. This role involves designing and improving highly available infrastructure for AI training, inference, and data-processing workloads while managing service-level objectives, capacity plans, and incident response. You will build monitoring, logging, tracing, and AI-specific observability for model-serving platforms. Key responsibilities include automating deployment, scaling, and disaster recovery while optimizing GPU utilization and cost efficiency. Required skills include experience with Linux, networking, cloud platforms like AWS, Azure, or Google Cloud, and infrastructure-as-code tools like Terraform. You must be proficient in Python or Go. The role addresses the technical challenge of making AI systems production-ready through MLOps practices, model versioning, and high-performance computing.

Skills

Kubernetes Terraform Python Go AWS Azure Google Cloud CI/CD Prometheus Grafana OpenTelemetry Helm CUDA MLOps Vector Databases Linux Networking distributed-systems Chaos Engineering Argo CD

What you'll do

  • Design and operate highly available infrastructure for AI training, inference, and data-processing workloads.
  • Build monitoring, logging, tracing, and AI-specific observability for model-serving platforms.
  • Lead incident response, root-cause analysis, and post-incident reviews to improve reliability.
  • Automate deployment, scaling, disaster recovery, and routine operational tasks.
  • Operate Kubernetes-based services, cloud platforms, GPU clusters, networking, and storage.
  • Optimize model-serving performance regarding latency, throughput, GPU utilization, and cost efficiency.
  • Develop safe automation and self-healing workflows for common production issues.
  • Establish operational standards for model versioning, canary releases, and rollback procedures.

What we're looking for

  • 4+ years of experience in SRE, infrastructure engineering, DevOps, platform engineering, or production engineering.
  • Strong experience in Linux, networking, cloud, 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.
  • Experience operating machine-learning or generative-AI workloads in production (preferred).

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