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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 change traceability (documented diffs, rollback procedures, decision records) and a persistent knowledge base for the platform.
Leadership & Influence
- Mentoring analysts, data scientists and engineers to use LLMs and AI coding assistants with rigor and measurable productivity gains.
- Influencing direction across Engineering, Network, Product, Care and Platform teams without direct authority.
- Writing executive-grade reports and briefings in the Verizon Design System (VDS), with automated generation of HTML, PDF and Google Slides and publication controls that block release if any figure regresses.
What we’re looking for…
You’ll need to have:
- Bachelor’s degree or four or more years of work experience.
- Six or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
- Experience inHome Broadband or telecom analytics spanning fixed wireless access and fiber, including gateway and extender device telemetry.
- Experience with streaming ingest and lakehouse architectures (Kafka, Spark).
- Experience in Python, SQL on very large datasets, Google Cloud Platform experience, including BigQuery.
- Experience with LLM APIs, prompt and context engineering, and building AI-powered internal tools and agents used by business stakeholders.
- Experience with AI coding assistants in production engineering, including custom agents, skills, hooks or MCP integrations.
Even better if you have one or more of the following:
- Experience in Python for production analytics services and automation (FastAPI, asyncio/concurrency, pyarrow/pandas) and expert SQL on very large datasets.
- Deep knowledge of TR-181 / TR-069 / USP data models, prplOS/OpenWrt gateway platforms, and Wi-Fi radio, station and backhaul (Wi-Fi, MoCA, Ethernet) 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 (Python, Bash, Google Apps Script, schedulers, CI/CD).
- Experience in delivering natural-language-to-SQL systems with a governed ontology and measured, reported accuracy.
- Experience with quality-of-experience and service-performance scoring, and turning model output into care-facing diagnostics.
- Micro-frontend architecture experience (React, Vite, Module Federation) on a shared design-system foundation, ideally VDS.
- Redis cache architecture for analytics APIs: key design, TTL strategy, warmers, distributed locks and lease recovery.
- Automated testing for analytics products: Playwright visual/E2E testing, Vitest, data-parity frameworks.
- Linux production operations (nginx, systemd, gunicorn/uvicorn), Bitbucket/Git workflows and OpenShift/Kubernetes.
- Cloud migration planning across GCP and AWS, with total-cost-of-ownership and ROI modeling.
- Experience with PostgreSQL/Citus, Tableau, Splunk or Amazon Athena.
- Experience with AI knowledge-management and design tools (e.g., NotebookLM, Google Stitch) at team scale.
- Expertise in mentoring cross-functional teams and shaping technical decisions across organizations.
Where you’ll be working
In this hybrid role, you'll have a defined work location that includes working from home and a minimum of three days per week in the office, which will be set by your manager. Employees are responsible for maintaining compliance with hybrid work policies.
Scheduled Weekly Hours 40
Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to veteran status, disability or other legally protected characteristics.
Benefits and Compensation Our benefits are designed to help you move forward in your career, and in areas of your life outside of Verizon. From health and wellness benefit options including: medical, dental, vision, short and long term disability, basic life insurance, supplemental life insurance, AD&D insurance, identity theft protection, pet insurance and group home & auto insurance. We also offer a matched 401(k) savings plan, up to 8 company paid holidays per year and up to 6 personal days per year, paid parental leave, adoption assistance and tuition assistance, plus other incentives, we’ve got you covered with our award-winning total rewards package. Depending on the role, employees have the opportunity to receive compensation in the form of premium pay such as overtime, shift differential, holiday pay, allowances, etc. Newly hired employees receive up to 15 days of vacation per year, which grows with additional service. For part-timers, your coverage will vary as you may be eligible for some of these benefits depending on your individual circumstances.
The salary will vary depending on your location and confirmed job-related skills and experience. This is an incentive based position with the potential to earn more. For part-time roles, your compensation will be adjusted to reflect your hours.