• I'm Venky Chivukula — eighteen-plus years in technology, and the throughline across all of it is productizing infrastructure: taking platform and network capabilities that engineers build and turning them into services with clear SLAs, a commercial model, and teams that actually adopt them. (pause)

Agentic AI Runtime Infrastructure — Gateways, Routing & Semantic Caching

• Most recently, as Director of Technical Product Management at a large enterprise, I own the product vision for a Kubernetes-as-a-Service and Internal Developer Platform that runs across 1,300+ engineering teams and 300+ production clusters. (pause)
• On top of that platform I productized the agentic AI runtime layer — AI coding assistants, intelligent code review, automated documentation, AI-assisted test generation — which lifted developer productivity and delivery velocity by 70%. (pause)
• The piece most relevant to this role is the AI Gateway: I led its rollout from lab to production as the single front door for internal GenAI traffic — standardized prompt routing, policy enforcement, and secure connectivity — so a new team could onboard repeatably instead of wiring up its own model access. (pause)
• Behind the gateway I added semantic caching — answering a repeated or near-identical prompt from cache instead of paying for another inference call — plus real-time token monitoring, which cut redundant inference and reduced prompt/response latency for the highest-traffic workflows. (pause)
• And I designed MCP-compliant context retrieval pipelines — a standard way for agents to pull the right internal context — which made developer onboarding 70% faster.

Intelligent Routing at Scale — Amazon Ads SSP

• Before that, four years as a Principal Technical Product Manager at Amazon, first on the Amazon Ads Supply-Side Platform powering video monetization for Prime Video and Freevee across 120 million-plus connected devices. (pause)
• I introduced AI Routers that dynamically routed traffic across caching tiers and content pipelines based on latency, load, and policy signals — and paired them with AI-driven caching and deduplication that took redundant payload processing from 40% down to 2% and improved inventory yield 70%. (pause)
• That platform handled billions of requests at 99.99% availability supporting $40 million-plus in monthly revenue, and I defined the partner-facing SLAs that tied those reliability targets to commercial outcomes.

Network Infrastructure — Data Center, SD-WAN, SASE & AI Inference Benchmarking

• On the AWS side, I drove infrastructure expansion into new regional Availability Zones — launch readiness and architectural standardization for 120+ core AWS services. (pause)
• I partnered with networking and security teams on high-density data center network design — capacity constraints, latency, East-West traffic patterns — and integrated SD-WAN and SASE frameworks into the regional design to optimize both East-West and North-South flows. (pause)
• And I led multi-cloud lab validation and benchmarking before production cutover, including network performance under peak AI inference workloads — which became the latency targets and capacity-planning models for those regions. Infrastructure readiness landed 100% on time, with zero Sev-1 incidents post-launch.

Commercial Lens — Market Analysis, Unit Economics & GTM

• Across those roles I've built roadmaps and commercial models from market analysis of public hyperscalers, edge nodes, on-premise data centers, and GPU-first NeoClouds — including GPU and token unit economics and AI services pricing. (pause)
• That's what turns an infrastructure capability into a product: knowing what it costs to serve a token, what the market charges for it, and what SLA a customer will pay a premium for.

Foundation — Zero Trust, Token Governance & Context

• Earlier, I led product for the CMS Federal Data Services Hub on AWS GovCloud — 16 million-plus users across seven-plus federal agencies under Zero Trust, FedRAMP, and SOC 2 — where I defined the token brokering and monitoring patterns for identity and access flows. (pause)
• At Hexaware I ran PoCs validating context retrieval pipelines for GenAI-assisted operations, and at CGI Federal I built early knowledge-graph models for cross-system data discovery — the same building blocks agentic systems rely on today.

Fit

• What connects all of it is exactly what this role describes: convergence of the network and the AI runtime into one fabric that can be sold with an SLA. (pause)
• From what I've read about Verizon AI Connect — positioning Verizon's fiber, edge, and data center footprint to carry AI inference workloads, including the NVIDIA partnership on GPU compute at the edge — the hard problem now is productizing that capacity: gateways, routing, caching, and MCP integrations that make the network AI-aware rather than just AI-adjacent. (pause)
• That's the layer I've been building on the platform side — and the network-infrastructure work at AWS is the other half of it.

Closing

• I'm genuinely excited about bringing agentic AI runtime product experience and enterprise network infrastructure depth together at Verizon — at the point where the network itself becomes the platform for autonomous agents.