Senior AI Engineer: Propensity & Personalization

Verizon; Boston (MA); Hybrid; USD 101,000 - 194,000; Full time

Benefits offered by this job

Hybrid work model

Health benefits

401(k) matching

Paid time off

Parental leave

Tuition assistance

Job summary

Verizon is seeking a Senior AI Engineer to lead the design, development, and deployment of agentic AI and propensity models. You will work within a modern, agile pod model to optimize customer journeys, reduce churn, and empower marketing decisions.

You will own end-to-end model pipelines, mentor junior data scientists, and present insights to senior leadership. A hybrid work setup includes office presence several days a week, with a strong emphasis on collaboration and impact.

Qualifications

• Bachelor's degree or four or more years of work experience.
• Four+ years of relevant experience demonstrated in ML projects.
• Experience with high-priority technical initiatives in a corporate setting.
• Knowledge of LLM agent architecture and RAG concepts.
• Proficiency in Python, R, and SQL for analytics.

Responsibilities

• Lead design, development, and optimization of agentic AI, LLM, and ML solutions.
• Build, train, and deploy complex propensity models to predict customer behavior and churn.
• Manage end-to-end model development pipelines from feature engineering to deployment.
• Mentor junior data scientists and provide technical guidance.
• Own lifecycle of models deployed in high-impact areas with continuous optimization.
• Present model insights and recommendations to senior leadership and partners.

Skills

Python

R

SQL

ML/DL

LLM

Pytorch

Education

Bachelor's degree or 4+ years experience

Master's degree (preferred)

Tools

LangChain

LangGraph

RAG

ONNX

Vector databases

Vertex AI / GCP

Job description

Verizon is seeking a Senior AI Engineer to lead the design, development, and deployment of agentic AI and propensity models. You will work within a modern, agile pod model to optimize customer journeys, reduce churn, and empower marketing decisions.

You will own end-to-end model pipelines, mentor junior data scientists, and present insights to senior leadership. A hybrid work setup includes office presence several days a week, with a strong emphasis on collaboration and impact.