Distinguished Engineer - ML/AI Engineer

Verizon•Alpharetta, GA

2d•$137,000 - $263,000•Hybrid

About The Position

As a Dist Engr-AI Engineer within our team of propensity modelers, you will be responsible for the technical execution of Verizon's transition to a modern, agile pod model. 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 six or more years of work experience.
Eight or more years of relevant experience required, demonstrated through work experience and/or military experience.
Experience working on high-priority or high-visibility technical initiatives within a corporate setting.
Experience designing and measuring A/B tests or uplift models for targeting.
Strong expertise and experience in machine learning/deep learning algorithms, including transformer, transfer learning, NLP, expertise in using Pytorch and scikit-learn packages, as well as probability calibration and time-based validation.
Experience with Python, R, and SQL for advanced statistical modeling and large-scale data extraction.
Experience with cloud platforms (e.g., GCP, AWS, or Azure) and production MLOps tools for tracking and maintaining model workflows.

Nice To Haves

Knowledge of LLM agent architecture: Google ADK, LangChain/LangGraph, multi-agent orchestration, retrieval-augmented generation RAG , MCP, A2A protocols, ONNX-based embeddings, vector databases and semantic search.
Knowledge of cloud-scale data engineering: Big Query pipelines, GCP (Cloud Run, Vertex AI).

Responsibilities

Leading the design, development, and optimization of agentic AI, LLM, and machine learning/deep learning solutions.
Building, training, and deploying our high-priority propensity models to predict customer behaviors, churn risk, and key lifecycle triggers.
Drive consolidation of overlapping propensity models into a shared customer state and unified models, reducing operational and development cost.
Operating independently to manage end-to-end model development pipelines, from advanced feature engineering to production deployment and monitoring.
Serve as a subject matter expert on agentic development, machine learning and predictive modeling, providing technical guidance and mentorship to junior data scientists.
Owning the lifecycle of models deployed in high-impact areas, ensuring continuous optimization, accuracy, and measurable business performance.
Presenting complex model insights, performance metrics, and strategic recommendations clearly to senior leadership and cross-functional business partners.