Nvidia Senior Site Reliability Engineer 

India, Karnataka, Bengaluru 

279549030

What you will be doing:

• Support and work on groundbreaking Generative AI inferencing workloads running in a globally-distributed heterogeneous environment spanning all major cloud service providers. Ensure the best possible performance and availability on current and next-generation GPU architectures.

• Collaborate closely with the service owner, architecture, research, and tools teams at NVIDIA to achieve ideal results for AI problems at hand.

• Monitoring & supporting critical high-performance, large-scale services running multi-cloud.

• Participate in the triage & resolution of sophisticated infra-related issues.

• Maintain services once live by measuring and monitoring availability, latency, and overall system health using metrics, logs, and traces.

• Scale systems sustainably through mechanisms like automation and evolve systems by pushing for changes that improve reliability and velocity.

• Practice balanced incident response and blameless postmortems.

• Be part of an on-call rotation to support production systems and lead significant production improvement around tooling, automation, and process.

• Architect, design, and code using your expertise to optimize, deploy and productize services.

What we need to see:

• 8+ years of experience operating & owning end-to-end availability and performance of mission-critical services in a live-site production environment, either as an SRE or Service Owner.

• 3+ years executing incident management and participating in an on call shift.

• BS degree in Computer Science or a related technical field involving coding (e.g., physics or mathematics), or equivalent experience

• Solid understanding of containerization and microservices architecture, K8s. Excellent understanding of the Kubernetes ecosystem and best practices with K8s.

• Ability to dissect complex problems into simple sub-problems and use available solutions to resolve them.

• Technical leadership beyond development that includes scoping, requirements capturing, leading and influencing multiple teams of engineers on broad development initiatives.

• Lead significant production activities, including change management, post-mortem reviews, workflow processes, software design, and delivering software automation in various languages (Python, or Go ) and technologies (CI/CD auto-remediation, alert correlation).

• Best in understanding SLO/SLIs, error budgeting, KPIs, and configuring for highly sophisticated services.

• Experience with the ELK and Prometheus stacks as a power user and administrator.

• Excellent understanding of cloud environments and technologies, especially AWS, Azure, GCP, or OCI.

• Proven strengths in identifying, mitigating, and root-causing issues while continuously seeking ways to drive optimization, efficiency, and the bottom line.

Ways to stand out from the crowd:

• Exposure to containerization and cloud-based deployments for AI models.

• Excellent coding: Python, Go (Any similar language).

• Prior experience driving production issues and helping with on-call support and understanding of Deep Learning / Machine Learning / AI.

• Experience with Cuda, PyTorch, TensorRT, TensorFlow, and/or Triton as well as experience with StackStorm and similar automation platforms is a bonus.

• Understanding of observability instrumentation techniques and best practices, including OpenTelemetry.