Technical Product Owner - Charlotte, NC (Hybrid)

Role : Technical Product Owner
Location : Charlotte, NC (Hybrid)
Contract

"Must be legally authorized to work in US without need for employer sponsorship now or at any time in the future.

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 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 Product Owner/Manager, preferably in infrastructure/platform products

Experience with Kubernetes, containerization, and cloud platforms (Google Cloud Platform)

Background in regulated industries (banking/financial services preferred)

Excellent communication skills with ability to bridge technical and business stakeholders

Preferred Qualifications

Advanced degree in Computer Science or related field

Cloud/ML certifications (Google Cloud Platform ML Engineer, NVIDIA DLI)

MLOps platform experience (MLflow, Kubeflow)

FinOps and cost management expertise

LLM training/inference optimization knowledge