Director - AI​/ML Engineering

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.

What you'll be doing...

The Director of AI/ML Engineering oversees the critical bridge between experimental AI science and highly scalable, production-grade systems with a heavy emphasis on Large Language Models (LLMs) and enterprise-level Generative AI applications. This leader will manage high-impact initiatives and a team of top-tier talent to deploy transformative AI solutions that optimize operational efficiency, lower cost barriers, and elevate both customer and employee experiences across VBG.

This role requires far more than technical stewardship; it demands a cultural change agent who naturally thinks outside the box and inherently operates with an innovation-first mindset with a goal of "getting things done". The successful candidate does not accept processes just because "that's how they've always been done." You must be willing to constructively challenge legacy tech debt, comfortable questioning traditional software development life cycles that slow down deployment, and ready to disrupt established boundaries to unlock true AI capabilities.

Instead of simply retrofitting AI into existing, outdated systems, you will champion an AI-native approach to engineering and operations.

This means pioneering entirely new ways of working by shifting teams from manual coding paradigms to agent-assisted development and replacing heavy, multi-turn human procedural bottlenecks with zero-shot or one-shot autonomous agent actions. It also requires transforming rigid, deterministic software gates into fluid, probabilistic AI-driven logic, while seamlessly infusing rapid experimentation, continuous testing, and failure-tolerant prototyping into the daily DNA of the engineering organization.

Key Responsibilities:

* AI Industrialization & Scaling:
Lead the deployment, operationalization, and maintenance of high-availability AI services; architect robust, reliable AI/ML pipelines capable of handling millions of concurrent requests.

* Compute & Latency Management:
Balance high-performance compute costs against the business value generated by models, ensuring systems satisfy the strict latency and scaling requirements of a global enterprise.

* Modern MLOps Foundations:
Establish rigorous engineering standards around MLOps, software SDKs, containerization, instrumentation, and distributed infrastructure to safely move experimental lab models into high-volume production.

* Ethical AI & Compliance:
Institutionalize strict standards for responsible AI, including safety, bias mitigation, data privacy, and compliance guardrails across all automated platforms

* Advanced Architectural Design:
Direct the end-to-end development, evaluation, and lifecycle management of enterprise GenAI platforms and application frameworks.

* Adaptive Model Routing:
Move the organization from monolithic model structures toward adaptive, specialized clusters by pioneering frameworks like Mixture of Experts (MoE) and multi-tasking systems that dynamically route queries based on domain expertise.

* Ecosystem Evaluation:
Autonomously evaluate evolving open-source and proprietary LLM frameworks, selecting optimal technologies, API design patterns, and orchestration engines while protecting enterprise data privacy.

* Digital Twin and Predictive Systems Oversee operational digital twins and simulation frameworks to model workflows, stress-test pre-production GenAI models and tools, and utilize predictive insights to transition enterprise systems from static decision support into autonomous, self-learning networks.

* People…