Technical Program Manager • Systems • AI Platforms

Ankur Shukla

Technical Program Management portfolio showcasing end-to-end execution across AI model evaluations, large-scale cloud infrastructure, multi-platform SDLC roadmaps, and capacity modeling.

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AI / LLM Systems Delivery

Model quality benchmarks, automated policy/safety filtering, and sub-50ms evaluation latency SLAs.

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Infrastructure & Capacity

GPU/TPU cluster fleet modeling, spot vs on-demand unit economics, and 99.99% availability headroom.

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Multi-Platform SDLC

Cross-surface execution across Desktop Web, Mobile Web, Android, and iOS with active blocker remediation.

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Data-Driven Experimentation

A/B flight management, metric telemetry, click-through rate optimization, and search session lift.

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Representative Case Studies: The interactive dashboards and systems architectures below are modeled using representative synthetic datasets designed to illustrate Technical Program Management execution, telemetry frameworks, and operational decision-making.

Flagship TPM Case Studies

Interactive platforms modeling real-world technical programs and telemetry dashboards.

🔍 Search Suggestion Intelligence Platform

AI Autocomplete Quality, Evaluation SLA, and Cross-Platform A/B Flights

AI / LLM Evaluation A/B Testing Pipeline SLA Multi-Platform SDLC

TPM Leadership & Scope: Search autocomplete and suggestion engines drive the primary entry point for user search journeys. This case study demonstrates how an end-to-end program governs AI-generated query candidates, filters spam and policy violations, and enforces a strict <50ms P95 evaluation latency SLA across daily batch rollups.

Cross-Platform Impact: Governs the live A/B flight rollout of v1.1 Contextual Category Chips across 58.55M DAU (Desktop, Mobile Web, Android, iOS), capturing a +6.8% CTR lift on Android, while driving active risk remediation on Safari and iOS.

58.55M

Total Daily Users (DAU)

+6.8% Lift

Max A/B CTR Gain

42.0ms

P95 Eval Latency SLA

14 Initiatives

SDLC Roadmap Tracked

⚡ AI Workload & Capacity Planning Platform

Accelerator Utilization, Spot vs. On-Demand Allocation & Unit Economics

Capacity Modeling GPU / TPU Clusters Cost Optimization Quota Forecasting

TPM Leadership & Scope: Scaling enterprise AI inference and LLM training workloads requires balancing the compute budgets against strict latency and availability SLAs. This program models compute supply and demand across 12 GPU/TPU accelerator clusters.

Cross-Platform Impact: Governs dynamic workload scheduling, spot/preemptible instance cost optimization (yielding a 4.2x cost efficiency factor), burst headroom buffering, and quota lead-time forecasting to guarantee 99.99% service availability without costly over-provisioning.

12 Clusters

GPU/TPU Fleets Modeled

4.2x

Unit Cost Efficiency

88.4%

Target Peak Utilization

99.99%

Service Availability SLA