Principal AI Engineer

Verizon•Basking Ridge, NJ

7d•Hybrid

About The Position

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... You will serve as a lead within our team of propensity modelers, driving the technical execution of Verizon’s transition to a modern, agile pod model. In this principal-level role, you will operate with a high degree of independence, owning our most complex propensity models and supporting Verizon's highest-priority, highest-visibility base management pods. You will work directly at the critical touchpoints of the customer lifecycle and high-impact trigger events, providing the advanced machine learning support needed to unlock deep microsegmentation and hyper-personalization. Your work will directly empower marketers, optimize customer journeys, and drive down churn across our most critical customer segments.

Requirements

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.
Four or more years of experience independently developing, deploying, and optimizing complex machine learning or propensity models in a production environment.
Experience working on high-priority or high-visibility technical initiatives within a corporate setting.
Knowledge of LLM agent architecture: Google ADK, LangChain/LangGraph, multi-agent orchestration, retrieval-augmented generation RAG , MCP, A2A protocols.
Experience with retrieval-augmented generation (RAG), ONNX-based embeddings, vector databases and semantic search
Knowledge of cloud-scale data engineering: BigQuery pipelines, GCP (Cloud Run, Vertex AI).
Experience with Python, R, and SQL for advanced statistical modeling and large-scale data extraction.

Nice To Haves

Master’s degree in Data Science, Computer Science, Statistics, or a highly quantitative field.
Strong domain expertise in customer retention strategies, churn prediction, or customer lifetime value (CLV) modeling.
Experience designing and evaluating A/B tests for models in production, production ML/LLM monitoring and observability tooling (e.g., OpenTelemetry, Arize Phoenix, Galileo) for tracking model drift, accuracy degradation, or output quality over time.
Experience operating within an agile, pod-based operating model, directly supporting marketing or customer experience teams.
Experience with cloud platforms (e.g., GCP, AWS, or Azure) and production MLOps tools for tracking and maintaining model workflows.
Excellent communication and presentation skills, with a demonstrated ability to explain advanced predictive models to non-technical stakeholders.

Responsibilities

Building, training, and deploying our most complex and high-priority propensity models to predict customer behaviors, churn risk, and key lifecycle triggers.
Operating independently to manage end-to-end model development pipelines, from advanced feature engineering to production deployment and monitoring.
Collaborating closely with high-visibility base management pods and senior marketing stakeholders to translate complex business retention goals into actionable data science solutions.
Owning the lifecycle of models deployed in high-impact areas, ensuring continuous optimization, accuracy, and measurable business performance.
Translating advanced analytical outputs into actionable microsegmentation strategies that enable marketing partners to deliver highly personalized customer experiences.
Serving as a technical mentor and advisor to senior and mid-level AI/ML engineers on the team, sharing best practices for model optimization and data pipelines.
Presenting complex model insights, performance metrics, and strategic recommendations clearly to senior leadership and cross-functional business partners.