• I'm Venky Chivukula — eighteen-plus years in technology, and the last several focused specifically on the seam this role sits on: taking AI models built by a central team and getting them into production features that downstream product teams actually ship, measure, and trust. (pause)

Central AI Models Consumed by Downstream Product Teams

• At Amazon, as Technical Program Manager for AI Platforms, I owned cross-functional delivery of central computer vision models — YOLO object detection, scene detection, and automated frame analysis — that the Prime Video and Freevee ad-insertion engines consumed. (pause)
• The structure was exactly what this role describes: a central AI research team built the models, video-processing engineers built the pipelines, and separate downstream product groups owned the features customers actually saw. Nobody in that chain reported to anyone else. (pause)
• My job was the seam — making sure a model improvement on the research side turned into a measurable change in the product, on platforms serving 120 million-plus users at 99.99% availability.

Linking Model Performance to Customer Outcome

• The research team measured success in Precision/Recall and inference latency; the ad business measured it in whether a viewer saw the same ad over and over. (pause)
• I tied those two together — so a recall improvement in scene detection could be traced to fewer repeated or badly placed ads — and that's what took ad creative redundancy from 40% down to 2%. (pause)
• That's the same problem as this role's AI Impact Tracker: a systemic link between model performance and customer outcome, with a baseline you can actually improve against.

Inference Optimization & Central Infrastructure Adoption

• I also managed rollout of the central, reusable platform infrastructure underneath those models — low-latency computer vision pipelines where we were optimizing inference throughput, latency, and accuracy at the same time. (pause)
• And the hard part of a central platform is never building it — it's adoption. Each downstream team had its own roadmap, so I ran the rollout as a program: onboarding sequence, shared readiness criteria, and a clear contract for what the central team guaranteed.

AI for Developer Practices & Executive Dashboards

• Today I'm Director of Technical Program Management for AI and Cloud Platforms. (pause)
• I drive our AI for Developer Practices program — integrating Generative AI, LLMs, and automated tooling into the internal software development lifecycle, tracking developer adoption, and measuring the impact on engineering velocity and software quality. (pause)
• I built the Internal Developer Platform and Kubernetes-as-a-Service across 300+ production clusters, which took deployment lead time from two weeks to under 45 minutes. (pause)
• And I own the executive-facing dashboards tracking AI adoption, operational efficiency, reliability, and organizational OKRs — where the job isn't the chart, it's making sure the number is right.

Pathfinding on Ambiguous AI Initiatives

• A recurring part of my current role is taking an emergent, genuinely ambiguous AI idea from leadership, running early stakeholder discovery, giving it a real definition — problem, user, success metric, staffing — and handing it off cleanly to an owning team once it's scoped. (pause)
• That's almost word-for-word the Emergent Strategic Initiatives piece of this role, including the handoff to a dedicated Product Manager.

Mission-Critical & Public-Sector Foundation

• Before Amazon, I spent six years at Optum delivering the CMS Federal Data Services Hub on AWS GovCloud — real-time, mission-critical data exchange across 7+ federal agencies including IRS, DHS, SSA, and VA, for 16 million-plus citizens — under zero-trust security and strict compliance. (pause)
• At AWS I also directed regional infrastructure expansion delivering three new Availability Zones. So 'it cannot go down' isn't a new constraint for me — it's the one I grew up in.

Fit

• What draws me to this role is that Motorola Solutions is applying AI in exactly the environment where getting it right matters most — first responders, 911 centers, video security. (pause)
• From what I've read, Motorola has been embedding AI across the portfolio — voice assistance on APX NEXT radios through the ViQi virtual partner, plus AI in command-center and video-security products — which means a central speech-and-language team with many downstream product consumers. (pause)
• To be direct: my hands-on model domain has been vision more than speech. But the program problem is the same one I've solved — a central model, many consuming product teams, and a technical metric that has to be translated into a customer outcome. For voice that metric is Word Error Rate instead of Precision/Recall, and the noisy audio on a radio channel replaces the cluttered video frame.

Closing

• I'd love to bring that combination — central AI model delivery, outcome-linked metrics, and mission-critical discipline — to Motorola's Voice AI organization, where the customer is someone whose job depends on the transcript being right.