Executive product leader for AI Factory platforms, owning product strategy, UX/CX direction, roadmap, KPI tree, and product operating model. I partner closely with Platform Engineering, SRE/NOC, Data Center Operations (co-lo), Storage/Network, and Security/Identity to deliver platform capabilities at scale. Engineering teams implement; I lead outcomes, alignment, and adoption across complex enterprise ecosystems.
 

PRODUCTS DELIVERED:
 

1. AI Factory control plane as a product (self-service platform experience):
   Defined and launched a control-plane experience that converts GPU/HPC infrastructure into a consumable platform, including tenant and workspace onboarding, RBAC and entitlements, quota and guardrail UX, environment provisioning, and workload submission. Reduced ticket-driven operations and accelerated time-to-first-run. 
    

2. Workload lifecycle experience with operational parity:
   Owned the end-to-end journey for training, inference, and batch workflows across data registration and ingress/egress, runtime and container profiles, job configuration, queue and priority visibility, checkpointing and retries, artifact and model handoff, and failure triage. Delivered role-based operator surfaces for platform, SRE, and co-lo operations so adoption aligns with real operating context.
    

3. Utilization and cost transparency surfaces (experience tied to infrastructure reality):
   Productized decision-grade experiences translating infrastructure telemetry into action, including GPU-hours, SM utilization, VRAM pressure and OOM risk, queue wait and time-to-first-run, job success rates, I/O latency and IOPS, throughput, network saturation, and tenant-level burn rate. Enabled capacity planning and showback/chargeback narratives.
    

4. Developer enablement as a product (“golden paths”):
   Drove adoption-ready enablement through an onboarding portal, golden templates, reference pipelines, integration playbooks, sandbox and test-harness patterns, and runbook-backed self-service documentation. Thus, driving standardized adoption over bespoke implementations. 
    

5. Hybrid consistency across co-lo, private, and cloud-adjacent environments:
   Defined stable experience patterns across environments, including data locality, private connectivity boundaries, tenant isolation, and policy enforcement, ensuring a predictable user experience despite heterogeneous infrastructure.
    

ENGINEERING & GOVERNANCE:
 

• Control-plane primitives and interface contracts (as product requirements):
  Defined canonical platform primitives and contracts, including Tenant, Project or Workspace, Identity and Role, Quota and Entitlement, Dataset, Job or Run, Artifact or Model, and Policy. Required deterministic workflow states, idempotent APIs, event schemas, and predictable degradation modes.
   

• Scheduler and orchestration transparency as a product guarantee:
  Converted orchestration complexity into user-visible, reliable behavior, including queue semantics, fairness and priority, backoff and retry, preemption policies, and clear explanations for job wait and failure states. The goal was for the platform to behave like a product, not a black box. 
   

• Observability as an experience layer
  Productized observability that ties infrastructure signals to user outcomes, including:

• Time-to-first-run and time-to-first-batch

• Queue time distributions by tenant and project

• Job success, retry, and OOM rates with failure categorization

• GPU utilization and idle time, VRAM utilization, and throttling indicators

• Storage I/O latency, throughput, and bottleneck diagnostics

• Network saturation, packet loss, congestion indicators, and hotspot nodes

• Actionable remediation paths, operator runbooks, and escalation routing
   

• SLO-driven reliability and operational readiness:
  Defined SLOs as acceptance criteria, including control-plane availability, provisioning latency, queue latency targets, completion reliability, and MTTR. Operationalized incident response, postmortem loops, escalation paths, and release gating.
   

• Tenant governance as product behavior:
  Implemented governance as platform features, including least-privilege RBAC, tenant isolation, quota policies, template versioning, controlled change windows, auditability, and cost guardrails.

 

PRODUCT MANAGEMENT & ENABLEMENT:
 

• 0→1 delivery (MVP control plane and first successful run):
  Led structured discovery with data science and ML teams, platform engineering, SRE, storage and network, security, and co-lo operations. Translated findings into a capability roadmap, MVP golden path, KPI tree, success criteria, and rollout plan. 
   

• 1→n scaling and NPI readiness:
  Built launch playbooks, enablement kits, training and communications, support tiering, readiness gates, and adoption instrumentation so multi-tenant expansion scales without creating support cliffs.
   

• Operating model and decision rights:
  Established intake and prioritization, roadmap governance, architecture review rituals, KPI review cadences, and cross-functional RACI across product and UX, platform engineering, SRE and NOC, data center operations, and security and identity.
   

• Long-Term Platform & Governance Roadmap:
  Defined a multi-year roadmap for AI platform governance, observability, and lifecycle controls, aligning near-term delivery velocity with long-term trust, auditability, and regulatory readiness across enterprise and regulated environments.
   

TECHNICAL DELIVERY CONTEXTS
 

• Kubernetes-first AI Factory:
  Productized onboarding, provisioning, entitlements, workload submission, and observability experiences for Kubernetes-based GPU platforms, including node pool and runtime profiles and tenant isolation patterns.
   

• Ecosystem signals surfaced in UX:
  Integrated distributed tracing, metrics, centralized logging, and SLO monitoring into product experiences, tying operational signals directly to platform KPIs.
   

• Slurm/HPC-first AI Factory:
  Productized queue and partition experiences and transparency layers for HPC scheduling, including fair-share, priority, preemption, and job arrays, enabling diagnosable and actionable performance bottlenecks.
   

• HPC context used in diagnostics:
  Represented NCCL behavior, high-performance I/O paths, RDMA-style data flows, and storage and network constraints directly within product experiences.
   

• Hybrid co-location and cloud-adjacent platforms:
  Delivered consistent experiences across co-lo and hybrid environments, including identity and access, tenant isolation, policy guardrails, data locality, ingress and egress, cost transparency, and controlled change—designed for enterprise and regulated contexts.

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OUTCOMES

Reduced time-to-first-run and queue wait times; increased job success rates and GPU utilization while reducing idle capacity and OOM failures. Eliminated I/O bottlenecks, improved MTTR and control-plane availability, reduced tickets per active user, drove cross-team and multi-tenant adoption, and lowered overall cost-to-serve.