Distinguished Engineer - ML/AI Engineer

At Verizon, this hybrid role will focus on building and scaling agentic AI, LLM, and machine learning solutions tied to customer propensity modeling. The Distinguished Engineer will lead end-to-end model development and help consolidate overlapping propensity signals into a unified customer state to improve customer journeys and reduce churn.

What you’ll do

• Lead the design, development, and optimization of agentic AI, LLM, and machine learning/deep learning solutions.
• Build, train, and deploy high-priority propensity models to predict customer behaviors, churn risk, and key lifecycle triggers.
• Drive consolidation of overlapping propensity models into shared customer state and unified models to reduce operational and development cost.
• Operate independently to manage end-to-end model development pipelines, from advanced feature engineering through production deployment and ongoing monitoring.
• Serve as a subject matter expert in agentic development, machine learning, and predictive modeling, providing technical guidance and mentorship to junior data scientists.
• Own the lifecycle of deployed models in high-impact areas, ensuring continuous optimization, accuracy, and measurable business performance.
• Present complex model insights, performance metrics, and strategic recommendations to senior leadership and cross-functional business partners.

Required qualifications

• Bachelor’s degree or six or more years of work experience.
• Eight or more years of relevant experience, demonstrated through work experience and/or military experience.
• Experience delivering high-priority or high-visibility technical initiatives within a corporate setting.
• Experience designing and measuring A/B tests or uplift models for targeting.
• Strong machine learning and deep learning expertise, including transformer, transfer learning, and NLP, plus experience with PyTorch and scikit-learn, as well as probability calibration and time-based validation.
• Hands-on experience with Python, R, and SQL for advanced statistical modeling and large-scale data extraction.
• Experience with cloud platforms such as GCP, AWS, or Azure and production MLOps tools for tracking and maintaining model workflows.

Technology stack

• Python, R, SQL
• PyTorch, scikit-learn
• Transformers, transfer learning, NLP
• Probability calibration, time-based validation
• A/B tests, uplift models
• GCP, AWS, Azure
• Production MLOps tools
• Google ADK
• LangChain/LangGraph, multi-agent orchestration
• Retrieval-augmented generation (RAG)
• MCP, A2A protocols
• ONNX-based embeddings, vector databases, semantic search
• BigQuery pipelines, Cloud Run, Vertex AI

Compensation and benefits

• Salary range: USD 137,000 - 263,000 per year (based on location and full-time schedule)
• Medical, dental, vision
• Short and long term disability
• Basic life insurance; supplemental life insurance; AD&D insurance
• Identity theft protection
• Pet insurance
• Group home & auto insurance
• Matched 401(k) savings plan
• Up to 8 company paid holidays per year
• Up to 6 personal days per year, paid
• Paid parental leave; adoption assistance
• Tuition assistance
• Premium pay options such as overtime, shift differential, holiday pay, allowances, etc.
• Up to 15 days of vacation per year for newly hired employees, which grows with additional service

Location: Alpharetta, GA (hybrid). Hybrid schedule includes work from home and a minimum of three days per week in the office set by your manager. Employees are responsible for maintaining compliance with hybrid work policies. Scheduled weekly hours: 40.