Principal Engineer - AI/ ML - Home Broadband Solutions

Verizon; Irving (TX); On-site; USD 190,000 - 240,000; Full time

Job summary

Verizon is seeking a Principal Engineer - AI/ML Engineering for Verizon Home Broadband. You will own architecture, standards and AI strategy for a high-scale device-telemetry analytics platform, turning gateway and extender heartbeats into actionable insights for executives, network engineering, product and care teams.

You will design end-to-end lakehouse-to-dashboard stacks, embed LLMs, and set validation and responsible-AI standards to ensure trustworthy AI outputs.

Qualifications

• Experience leading AI-enabled analytics platforms.
• Ability to design modular analytics domains and APIs.
• Experience with on-prem, cloud, and multi-cloud deployment patterns.

Responsibilities

• Set and own the multi-year technical roadmap for an AI-enabled Home Broadband analytics platform.
• Architect modular analytics domains: customer, gateway, extender, broadband health, support, usage and alerting.
• Lead platform-placement decisions across on-prem, OpenShift/Kubernetes, GCP and AWS.

Skills

AI/ML engineering

Architecture

OpenShift/Kubernetes

Python services

LLM engineering

Tools

OpenShift/Kubernetes

Google Cloud Platform

AWS

Dremio

Apache Iceberg

Kafka-Spark

Job description

When you join Verizon

You want more out of a career. A place to share your ideas freely - even if they're daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love - driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together - lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife.

What you'll be doing...

As a Principal Engineer - AI/ML Engineering for Verizon Home Broadband, you will own the architecture, engineering standards and AI strategy of its device-telemetry analytics platform across 5G Home (FWA) and Fios. The platform turns tens of millions of gateway and extender heartbeats per day into governed, sub-second insight for executives, network engineering, product and customer care.

This role designs the lakehouse-to-dashboard stack end to end, embeds large language models where they create measurable value, and sets the validation and responsible-AI standards that keep every AI-generated number trustworthy. The role will be responsible for the distributed query engines, streaming data, broadband device data models and production LLM engineering, along with the judgment to explain trade-offs to senior leaders.

You will be responsible for the following but not limited to:

Technical Strategy & Platform Architecture:

• Setting and owning the multi-year technical roadmap for an AI-enabled Home Broadband analytics platform, tied to subscriber growth, device quality, care-cost reduction and churn.




• Architecting a modular platform of independently deployable analytics domains: customer, gateway, extender, broadband health, support, usage and alerting. Each is a Python service paired with a federated front end on a shared design-system foundation.




• Leading platform-placement decisions across on-premises infrastructure, OpenShift/Kubernetes, Google Cloud Platform and AWS, including capacity sizing, high-availability topology, cost modeling and build-versus-buy analysis.

Lakehouse, Query & Streaming Engineering:

• Owning the analytical serving layer on Dremio over Apache Iceberg, fed by Kafka- Spark streaming ingest.




• Defining table-design standards: partition and bucketing specs, compaction/OPTIMIZE trade-offs, file-layout effects on point-lookup and fleet-scan latency, and windowing aligned to Iceberg metadata horizons rather than raw event timestamps.




• Integrating BigQuery and other cloud warehouses where they fit the workload better, with consistent semantics and governance across engines.

AI, LLM & Agentic Systems:

• Leading adoption of agentic software engineering across the organization: multi-agent development workflows, custom skills, lifecycle hooks and Model Context Protocol (MCP) integrations in AI coding assistants (e.g., Claude Code, GitHub Copilot), with human approval gates on every production change.




• Engineering production LLM systems for accuracy, latency and cost: structured outputs, tool use, prompt caching, retrieval over curated examples, and ongoing accuracy measurement against labeled query sets.




• Using AI to compress delivery cycles for analytics: automated data-parity verification, AI-generated end-to-end and visual regression suites, automated performance audits, and design-to-code generation of dashboards.

Broadband Telemetry & Applied Analytics:

• Acting as the technical authority on TR-181 / TR-069 / USP device data across the organization, including AppleOS-based platforms and Wi‑Fi 6/6E/7 cohorts.




• Turning raw heartbeat, radio, station, WAN and event telemetry into rigorous metrics. That means correct counter-delta handling, direction-correct throughput, device-clock versus cloud-clock time, settle-time-aware freshness, and codebook-accurate reboot and crash classification.




• Building fleet-scale anomaly detection and classification: identifying customer-owned third‑party routers from hostname, IEEE OUI vendor and traffic signatures; detecting extender reboot loops; detecting ingest stalls from arrival‑clock analysis; and finding firmware regressions through build‑level splits.




• Turning quality‑of‑experience and service‑performance model outputs into explainable, care‑ready root‑cause indicators and home‑health scores.




• Delivering lifecycle insight for 5G Home and Fios (acquisition, activation, usage, device health, care contacts, retention, churn) through KPI frameworks built for executive decisions.

Performance, Reliability & Operational Excellence:

• Guarantee a sub-second render SLA on high‑traffic executive and care dashboards. Tools include pre-computed cache warming (Redis plus in-process caches), leader‑elected warmup, stale‑while‑revalidate persistence, bounded concurrency, outer deadlines, consolidated circuit breakers and saturation‑aware retry policy.




• Running disciplined performance engineering: concurrent‑user load simulation with cold-cache proof, per-query phase timelines.




• Setting production standards: environment isolation, Git‑based deploys, zero‑downtime rolling releases, production fingerprinting before deployment, and post‑deploy verification.

Governance, Validation & Responsible AI

• Writing and enforcing the organization’s standards for trustworthy analytics and responsible AI




• Ensuring every AI‑generated artifact is checked against source data before release.




• Maintaining complete