Technical Product Owner

Role: Technical Product Owner

Location: Charlotte, NC (Hybrid)

Hire type: Contract

Overview

Lead GPU/TPU monitoring and optimization initiatives for a large global bank s core technology unit. Drive performance improvements, cost reduction, and resource efficiency across AI/ML infrastructure through technical expertise and product ownership.

Key Responsibilities

• Own product roadmap for GPU/TPU monitoring solutions aligned with AI infrastructure strategy
• Define monitoring strategies covering utilization, performance, power, memory, and thermal metrics
• Analyze accelerator usage patterns and identify optimization opportunities to improve efficiency
• Collaborate with ML engineers to tune workloads and reduce training/inference latency
• Implement cost optimization strategies targeting 20 30% reduction through better resource allocation
• Manage product backlog, write user stories, and lead agile development cycles
• Partner with stakeholders across data science, infrastructure, and business units
• Ensure solutions meet banking security, compliance, and governance requirements
• Report on KPIs: utilization rates, cost savings, performance improvements, and SLA adherence

Required Qualifications

• 5+ years of experience with GPU/TPU infrastructure for AI/ML workloads
• Deep knowledge of NVIDIA GPUs (A100, H100) and Google TPUs
• Proficiency with monitoring tools: DCGM, nvidia-smi, Prometheus, Grafana
• Strong understanding of CUDA, PyTorch, TensorFlow, and distributed training
• 3+ years as a Product Owner/Manager, preferably in infrastructure or platform products
• Experience with Kubernetes, containerization, and cloud platforms (Google Cloud Platform)
• Background in regulated industries (banking or financial services preferred)
• Excellent communication skills with the ability to bridge technical and business stakeholders

Preferred Qualifications

• Advanced degree in Computer Science or a related field
• Cloud/ML certifications (Google Cloud Platform ML Engineer, NVIDIA DLI)
• MLOps platform experience (MLflow, Kubeflow)
• FinOps and cost management expertise