Site Reliability Engineer

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We are seeking a highly skilled and experienced Senior Engineer to join our team, focusing on the design and development of AI services and capabilities tailored to IT's GPU-based AI Clusters observability. This role involves reshaping how we lead alerts, metrics, and logs by introducing deep learning and GenAI to enhance reliability services. The ideal candidate will have a strong background in artificial intelligence, machine learning, and GPU-based AI infrastructure, with a consistent record of delivering innovative solutions that enhance system monitoring, performance, and reliability.

• Design, build, and maintain observability systems for leading NVIDIA DGX clusters, ensuring flawless monitoring of AI workloads, hardware utilization (GPUs), and system health.
• Develop monitoring tools and dashboards that supervise key metrics such as GPU utilization, memory, temperature, latency, network bandwidth, model performance, and system availability.
• Build custom alerting systems for AI/ML workflows, enabling proactive issue detection (e.g., GPU failures, hardware bottlenecks, system crashes).
• Collaborate with IT and MLOps teams to design efficient, scalable solutions for deploying, monitoring, and leading machine learning models on DGX systems.
• Optimize DGX infrastructure by implementing standard processes for observability, ensuring high performance and reducing operational costs.
• Supervise system-level metrics such as hardware temperature, power consumption, and GPU/CPU health, preventing hardware degradation or failure.
• Develop solutions for supervising AI/ML model performance across DGX clusters, integrating logging and supervising for model training, inference, and deployment processes.
• Integrate observability tools (e.g., Prometheus, Grafana, Splunk) with NVIDIA-specific tools (e.g., DCGM, NVIDIA GPU Cloud) for real-time monitoring and alerting.
• Work closely with data scientists and machine learning engineers to ensure effective resource utilization and model observability, including the identification of performance bottlenecks and tuning for optimal GPU usage.
• Drive solving and root cause analysis for failures and anomalies in both the DGX hardware and AI/ML models running on the infrastructure.
• Ensure compliance with ethical AI standards by monitoring fairness, model drift, and performance consistency.
• Document standard methodologies and processes for managing, deploying, and monitoring AI workloads on DGX clusters.

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or related fields.
• 7+ years of experience software engineering, systems engineering, or DevOps roles.
• 3+ years of experience in high-performance computing (HPC) or AI/ML environments.

• Strong experience leading NVIDIA DGX systems or similar GPU-based computing clusters.
• Proficiency in GPU monitoring tools such as NVIDIA Data Center GPU Manager (DCGM) and related NVIDIA libraries/APIs.
• Experience with AI/ML model deployment and monitoring on large-scale infrastructure, including model performance metrics (latency, throughput, accuracy).
• Hands-on experience with observability tools such as Prometheus, Grafana, Splunk or similar, especially in high-performance computing environments.
• Proficiency in scripting/programming languages (e.g., Python, Bash, Go) for automating cluster management and monitoring tasks.
• Experience with container orchestration technologies (e.g., Docker, Kubernetes), including NVIDIA's GPU operator for Kubernetes.
• Familiarity with AI/ML lifecycle management tools such as ML flow, Kubeflow, or similar.
• Strong understanding of HPC environments, including distributed computing, storage, and networking for AI/ML workloads.
• Experience with infrastructure monitoring and solving at both hardware (GPU, CPU, memory) and software (AI/ML models, applications) levels.
• Strong analytical and problem-solving skills, with the ability to interpret complex data and develop actionable insights.
• Excellent verbal and written communication skills, with the ability to convey technical concepts to non-technical partners.
• Ability to work effectively in a collaborative team environment and lead multiple projects simultaneously.
• Experience with NVIDIA NGC (NVIDIA GPU Cloud) and DGX OS software stack for large-scale AI workloads.
• Understanding of AI workload orchestration with frameworks such as Slurm or Kubernetes in GPU-based clusters.
• Knowledge of NVIDIA Deep Learning frameworks (TensorFlow, PyTorch) and their performance optimization on DGX infrastructure.
• Experience with AIOps tools for automated anomaly detection and solving of large-scale AI infrastructure.
• Certification or experience with cloud platforms that offer GPU instances (AWS, GCP, Azure).
• Familiarity with network performance tuning in HPC environments and large-scale AI workloads.
• Familiarity with DevOps practices and tools, including CI/CD pipelines and infrastructure as code. Knowledge of Graphs, Graph DB's and Graph Theory. Familiarity with Terraform, Helm Chart, Ansible, or similar tools.

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