Princ Engr-AI/ML Engineering

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…

Drive the development and implementation of AI-infused network use cases and ML solutions with cross-functional teams to deliver business value. Provide technical expertise, strategic thinking, and cross-functional collaboration skills to drive innovation in AI/ML capabilities across the organization. Guide the modernization of existing AI apps or build new ones. Work closely with Product Management and Data Scientists/Data Engineers to build and constantly drive excellence. Responsible for implementation, performance tuning, and scaling AI/ML applications. Provide guidance to the team with regards to planning, configuring, deploying, and operating cloud solutions. Contribute to the company’s multi-year journey to industrialize data science and AI capabilities across operations. Ensure AI fuels all decisions and business processes across the company, potentially scaling from billions to trillions of automated, real-time predictions. Lead the charter to industrialize AI/ML model development, feature engineering, model validation, deployment, and model observability. Mentor and guide teams in modeling and delivery of AI/ML solutions. Participate in internal and external forums to promote innovation and knowledge sharing. Articulate the story behind AI/ML solutions at every stage through executive presentations.

As a Principal AI/ML Engineer, you will leverage modern developer environments, tools, systems to rapidly diagnose issues,  multi-agent orchestration, and build scalable systems that consume and work with our internal model provisioning environment as endpoints to accelerate transformation across the business.

You sit at the intersection of AI architecture, rapid software prototyping, developer toolchains, and agent based capabilities turning complex business challenges into production-ready agents and intelligent tooling.

• Leading AI and analytical systems to solve AI & Big Data problems that drive customer satisfaction throughout our business & internal support systems.
• Investigating, designing, testing and implementing efficient software solutions.
• Rapid Prototyping & CLI-Driven Remediation: Use advanced AI-augmented developer tooling to rapidly prototype agents, diagnose integration hurdles, and deploy robust fixes.
• Local Orchestration & Systems Audits:  Build, configure, and audit local agent orchestrators, tool definitions, and developer environments to keep team workflows fast and compliant, with integration of automation.
• GCP & Cloud Integration:  Support non-production through to production cloud integrations, service endpoint configurations, and API consumption across Google Cloud Platform and internal hybrid infrastructures.

What we’re looking for…

You will 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 in Python programming, Git, enterprise systems, open-source tools, and the progression of Gen AI.
• Experience in AI platforms, GCP, and SQL.

Even better if you have one or more of the following:

• Enterprise Architecture or platform management experience.
• Model Routing & Cost Optimization: Practical experience benchmarking, quantizing, and routing workloads between API-based frontier models and self-hosted/open-source models as we look towards ways to minimize token spend.
• Hybrid Cloud & On-Prem Compute: Understanding of on-prem GPU inference serving alongside GCP cloud services.
• Knowledge of Development Lifecycle Management, DevOps Automation methods and practices, Post-Production Model Monitoring and End-to-End Architecture.
• Advanced knowledge of ai/ml engineering concepts and applied knowledge of practices and me…