Principal AI Engineer

Verizon Verizon · Telecom · Basking Ridge, NJ +2

Principal AI Engineer at Verizon focused on building and deploying complex propensity models for customer retention and personalization. The role involves end-to-end model development, feature engineering, production deployment, monitoring, and mentoring. It also requires knowledge of LLM agent architecture, RAG, vector databases, and cloud-scale data engineering.

What you'd actually do

  1. Building, training, and deploying our most complex and high-priority propensity models to predict customer behaviors, churn risk, and key lifecycle triggers.
  2. Operating independently to manage end-to-end model development pipelines, from advanced feature engineering to production deployment and monitoring.
  3. Collaborating closely with high-visibility base management pods and senior marketing stakeholders to translate complex business retention goals into actionable data science solutions.
  4. Owning the lifecycle of models deployed in high-impact areas, ensuring continuous optimization, accuracy, and measurable business performance.
  5. Translating advanced analytical outputs into actionable microsegmentation strategies that enable marketing partners to deliver highly personalized customer experiences.

Skills

Required

  • Python
  • R
  • SQL
  • LLM agent architecture
  • LangChain
  • LangGraph
  • multi-agent orchestration
  • RAG
  • vector databases
  • semantic search
  • BigQuery pipelines
  • GCP
  • Cloud Run
  • Vertex AI
  • machine learning
  • propensity models
  • feature engineering
  • production deployment
  • model monitoring

Nice to have

  • Master's degree in Data Science, Computer Science, Statistics, or a highly quantitative field.
  • Customer retention strategies
  • churn prediction
  • customer lifetime value (CLV) modeling
  • A/B testing
  • ML/LLM monitoring and observability tooling
  • OpenTelemetry
  • Arize Phoenix
  • Galileo
  • agile, pod-based operating model
  • GCP
  • AWS
  • Azure
  • MLOps tools
  • communication skills
  • presentation skills

What the JD emphasized

  • Four or more years of experience independently developing, deploying, and optimizing complex machine learning or propensity models in a production environment.
  • 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).

Other signals

  • customer retention
  • churn prediction
  • personalization
  • LLM agent architecture
  • RAG