Principal Engineer - AI/ ML - Home Broadband Solutions

Verizon•Irving, TX

9d•Hybrid

About The Position

As a Principal Engineer - AI/ML Engineering for Verizon Home Broadband, this role owns the architecture, engineering standards, and AI strategy for the device-telemetry analytics platform across 5G Home (FWA) and Fios. The platform processes tens of millions of gateway and extender heartbeats per day into governed, sub-second insights for executives, network engineering, product, and customer care. The role designs the lakehouse-to-dashboard stack, applies large language models where they provide measurable value, and establishes validation and responsible-AI standards to ensure AI-generated metrics are trustworthy. The position leads distributed query engines, streaming data, broadband device data models, and production LLM engineering, while communicating technical trade-offs to senior leaders. It sets the multi-year platform roadmap and architecture, leads lakehouse, query, and streaming engineering, develops AI and agentic systems, and turns broadband telemetry into applied analytics and explainable customer-care indicators. It also establishes performance, reliability, operational, governance, and validation standards; mentors cross-functional teams; and influences Engineering, Network, Product, Care, and Platform teams. This is a hybrid role with a defined work location that includes working from home and at least three days per week in the office, as set by the manager. Scheduled weekly hours are 40.

Requirements

Bachelor’s degree or four or more years of work experience.
Six or more years of relevant experience, demonstrated through work and/or military experience or specialized training.
Experience in Home Broadband or telecom analytics spanning fixed wireless access and fiber, including gateway and extender device telemetry.
Experience with streaming ingest and lakehouse architectures, including Kafka and Spark.
Experience with Python and SQL on very large datasets.
Experience with Google Cloud Platform, including BigQuery.
Experience with LLM APIs, prompt and context engineering, and building AI-powered internal tools and agents used by business stakeholders.
Experience using AI coding assistants in production engineering, including custom agents, skills, hooks, or MCP integrations.

Nice To Haves

Production analytics services and automation experience with Python, including FastAPI, asyncio/concurrency, and pyarrow/pandas, plus expert SQL on very large datasets.
Deep knowledge of TR-181, TR-069, and USP data models; prplOS/OpenWrt gateway platforms; and Wi-Fi radio, station, and backhaul telemetry.
Production experience with Dremio on Apache Iceberg, including query-performance diagnosis at the engine and file-layout level.
Working knowledge of data governance, model and output validation, and responsible-AI practices.
Workflow automation experience using Python, Bash, Google Apps Script, schedulers, or CI/CD.
Experience delivering natural-language-to-SQL systems with a governed ontology and measured, reported accuracy.
Experience with quality-of-experience and service-performance scoring and translating model outputs into care-facing diagnostics.
Micro-frontend architecture experience with React, Vite, or Module Federation on a shared design-system foundation, ideally VDS.
Redis cache architecture experience for analytics APIs, including key design, TTL strategy, warmers, distributed locks, and lease recovery.
Automated testing experience for analytics products, including Playwright visual/E2E testing, Vitest, and data-parity frameworks.
Linux production operations experience with nginx, systemd, gunicorn/uvicorn, Bitbucket/Git workflows, and OpenShift/Kubernetes.
Cloud migration planning across GCP and AWS, including total-cost-of-ownership and ROI modeling.
Experience with PostgreSQL/Citus, Tableau, Splunk, or Amazon Athena.
Experience with AI knowledge-management and design tools such as NotebookLM or Google Stitch at team scale.
Expertise in mentoring cross-functional teams and shaping technical decisions across organizations.

Responsibilities

Own the multi-year technical roadmap and architecture for an AI-enabled Home Broadband analytics platform, aligned with subscriber growth, device quality, care-cost reduction, and churn.
Architect modular analytics domains and a federated front end on a shared design-system foundation.
Lead platform-placement decisions across on-premises infrastructure, OpenShift/Kubernetes, Google Cloud Platform, and AWS, including capacity sizing, high availability, cost modeling, and build-versus-buy analysis.
Own the analytical serving layer on Dremio over Apache Iceberg, fed by Kafka-Spark streaming ingest; define table-design and query-performance standards.
Integrate BigQuery and other cloud warehouses where appropriate, maintaining consistent semantics and governance across engines.
Lead adoption of agentic software engineering, including multi-agent workflows, custom skills, lifecycle hooks, and MCP integrations, with human approval gates for production changes.
Engineer production LLM systems for accuracy, latency, and cost using structured outputs, tool use, prompt caching, retrieval over curated examples, and ongoing accuracy measurement.
Use AI to accelerate analytics delivery through automated data-parity verification, generated end-to-end and visual regression tests, performance audits, and dashboard design-to-code generation.
Serve as the technical authority on TR-181, TR-069, and USP device data, including AppleOS-based platforms and Wi-Fi 6/6E/7 cohorts.
Convert raw device telemetry into rigorous metrics, accounting for counter deltas, throughput direction, device and cloud clocks, freshness, and reboot and crash classifications.
Build fleet-scale anomaly detection and classification, including identification of third-party routers, extender reboot loops, ingest stalls, and firmware regressions.
Translate quality-of-experience and service-performance model outputs into explainable, care-ready root-cause indicators and home-health scores.
Deliver lifecycle insights for 5G Home and Fios through executive-focused KPI frameworks covering acquisition, activation, usage, device health, care contacts, retention, and churn.
Meet sub-second rendering SLAs for high-traffic executive and care dashboards using caching, warmup, bounded concurrency, deadlines, circuit breakers, and saturation-aware retries.
Conduct performance engineering, including concurrent-user load simulation with cold-cache proof and per-query phase timelines.
Set production standards for environment isolation, Git-based deployments, zero-downtime rolling releases, production fingerprinting, and post-deployment verification.
Write and enforce standards for trustworthy analytics and responsible AI; ensure AI-generated artifacts are checked against source data before release.
Maintain complete change traceability, including documented diffs, rollback procedures, decision records, and a persistent platform knowledge base.
Mentor analysts, data scientists, and engineers in rigorous use of LLMs and AI coding assistants, with measurable productivity gains.
Influence decisions across Engineering, Network, Product, Care, and Platform teams without direct authority.
Write executive-grade reports and briefings in the Verizon Design System, automate HTML, PDF, and Google Slides generation, and apply publication controls that block release if figures regress.

Benefits

Medical insurance
Dental insurance
Vision insurance
Short- and long-term disability insurance
Basic and supplemental life insurance
AD&D insurance
Identity theft protection
Pet insurance
Group home and auto insurance
Matched 401(k) savings plan
Up to 8 company-paid holidays per year
Up to 6 personal days per year
Paid parental leave
Adoption assistance
Tuition assistance
Other incentives
Potential premium pay, including overtime, shift differential, holiday pay, and allowances, depending on role
Up to 15 days of vacation per year for newly hired employees, increasing with service