Engr III Spec-AI Science

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.

We are looking for a highly skilled Engineer III, AI Science to design, build, and scale our next generation of AI systems. In this senior technical role, you will be a key driver in transitioning our machine learning capabilities from traditional predictive modeling to advanced Agentic and Prescriptive AI paradigms.

You will be hands-on in developing autonomous AI agents, large language model (LLM) pipelines, and reinforcement learning engines that optimize customer lifecycles, automate complex workflows, and drive measurable business value. You will also serve as a technical lead on high-impact projects, mentoring junior engineers and shaping our internal AI engineering standards.

Key Responsibilities

• Agentic AI & LLM Development
• Design, build, and deploy multi-agent systems and autonomous workflows using modern frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
• Develop robust LLM-based applications capable of reasoning, tool-use, and executing complex, multi-step tasks for dynamic customer interactions and internal automation.
• Implement guardrails, evaluation frameworks (LLM-as-a-judge), and prompt-engineering best practices to ensure safe, reliable, and compliant agent behavior in production.
• Predictive & Prescriptive Modeling
• Build state-of-the-art predictive models (deep learning, sequence models, tree-based models) for forecasting customer behavior, churn, propensity, and lifetime value (CLTV).
• Develop prescriptive optimization engines using Reinforcement Learning (RL), Contextual Bandits, or mathematical optimization to power real-time “Next Best Action” (NBA) and dynamic pricing recommendations.
• Apply causal inference and uplift modeling techniques to measure and optimize the true incremental impact of AI-driven interventions.
• Production Engineering & MLOps
• Collaborate with MLOps and Data Engineering teams to deploy machine learning and agentic models into highly scalable, low-latency production environments.
• Write production-grade, optimized code (Python) and build robust API endpoints for AI service integration.
• Design and maintain telemetry for monitoring model drift, agent hallucination rates, and overall system performance.
• Technical Leadership & Collaboration
• Lead the end-to-end technical execution of complex AI projects, from ideation and prototyping to production deployment.
• Mentor Engineer I and II team members, conduct rigorous code/model reviews, and foster a culture of continuous learning.
• Partner closely with Product Managers, Data Scientists, and Business Stakeholders to translate complex business requirements into scalable AI solutions.

Required Qualifications

• Education: Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.
• Experience: 5+ years of applied industry experience in machine learning, AI science, or data engineering (or 3+ years with a Ph.D.).
• Technical Skills:Advanced proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-Learn, XGBoost/LightGBM).Hands-on experience building and deploying LLMs and Agentic AI (LangChain, OpenAI APIs, open-source models like Llama 3/Mistral, RAG architectures).Strong foundation in Predictive and Prescriptive ML methodologies (time-series, reinforcement learning, optimization algorithms).Solid SQL skills and experience with big data processing (Spark, Databricks, Snowflake).
• Engineering Best Practices: Experience with cloud computing platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines for ML.

Preferred Qualifications

• Demonstrated experience building AI engines specifically for Base Management, Marketing Tech, or Customer Value Management (CVM).
• Experience with Causal Machine Learning (DoWhy,…