Senior Software Engineer, AIOps

NVIDIA

Senior Software Engineer building NVIDIA's distributed AIOps platform for GPU telemetry and predictive AI. Developing scalable model-serving infrastructure, observability, and diagnostic systems.

Posted 9/7/2026full-timeRaanana • 🇮🇱 IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and building AIOps systems, with a strong focus on high-performance distributed systems and ML model-serving infrastructure. Proficient in leveraging diverse database technologies and orchestrating telemetry data for real-time analysis and intelligent alerting.

Highest-signal resume keywords

Expert-Level Proficiency In GoC++ Or Rust ProgrammingKubernetes And Container-Based DeploymentsML Model-Serving Platforms Or MLOps ToolingProduction Distributed Systems Experience

ATS Keywords

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Hard Skills

AIOps System ArchitectureDistributed Systems DesignTelemetry Ingestion And ProcessingData Storage StrategiesPerformance Improvement MetricsModel-Packaging And VersioningReal-Time AnalysisDebugging With Logs And TracesData Movement Across NetworksHigh-Performance Concurrent Architectures

Soft Skills

Problem SimplificationAdaptability In Fast-Moving Environments

Certifications & Qualifications

B.Sc./M.Sc. In Computer ScienceComputer Engineering

Industry Keywords

AI ClustersTelemetry StreamsProduction EnvironmentInternal Tools DevelopmentEnterprise Customer Solutions

Tech Stack

Tools & technologies

C++Distributed SystemsGoKubernetesRust

About the role

Key responsibilities & impact

Architect and build an agentic AIOps system that monitors GPU fleet health, aggregates and correlates telemetry streams, surfaces intelligent alerts, and orchestrates diagnostic workflows and corrective actions
Research, evaluate, and prototype data storage strategies and data representations across diverse database technologies and modalities
Design distributed systems for high-density telemetry ingestion, processing, and real-time analysis in large-scale AI clusters
Instrument services with metrics, logs, and traces for rapid debugging and continuous performance improvement
Build and own model-serving infrastructure, including packaging, versioning, deployment, and monitoring of AI models in SaaS and on-premises environments
Contribute to core platform libraries and abstractions that accelerate development across the AIOps engineering team

Requirements

What you’ll need

B.Sc./M.Sc. in Computer Science, Computer Engineering, or a related technical field
8+ years of software engineering experience building production distributed systems
Expert-level proficiency in Go, C++, or Rust, with a focus on high-performance, concurrent architectures
Solid understanding of Kubernetes and container-based deployments for production services
Experience deploying, monitoring, and maintaining ML models or data-intensive services in a production environment
Comfort working in ambiguous, fast-moving environments where the product is still being shaped
Experience building ML model-serving platforms or MLOps tooling at scale
Track record of taking systems from prototype to stable, production-grade platform serving real enterprise customers
Understanding of the full stack, from data movement across the wire to processing in a distributed cluster
Ability to simplify complex problems and build internal tools or frameworks that empower engineering teams

Benefits

Comp & perks

Competitive salaries
Generous benefits package