Director - AI/ML Engineering

Verizon•Alpharetta, GA

1d•$143,500 - $275,000•Hybrid

About The Position

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 Instead of simply retrofitting AI into existing, outdated systems, you will champion an AI-native approach to engineering and operations.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

Requirements

Bachelor’s degree or four or more years of work experience.
Ten or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
Ten or more years of experience in AI/ML Engineering, Software Engineering, or AI Science environments.
Six or more years of progressive technical people leadership experience (Director or high-level Manager) directing multi-disciplinary teams of data scientists and engineering specialists.
Generative AI experience including technical fluency across the modern ML stack, with knowledge of LLMs, agentic orchestration, prompt engineering, vector databases, and multi-agent systems.

Nice To Haves

Scale and Deployment Track Record. Proven success building and scaling AI industrialization frameworks, modern MLOps pipelines, distributed training methodologies, and detailed simulation or digital twin networks.
Change Management and Influence experience. A clear history of introducing alternative, highly productive methodologies to a traditional enterprise environment.
Exceptional communication skills with a proven history of collaborating directly with VP and C-suite leaders to fund, execute, and scale disruptive technological solutions.

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 leadership & talent development: Build, manage, and scale a highly technical, talent-dense organization of senior AI Scientists, Machine Learning Engineers, and automation developers.
Culture of Excellence: Foster a progressive engineering environment dedicated to continuous testing, rapid prototyping, and staying abreast of state-of-the-art academic and industry AI research.
Engineering Architecture: Holds autonomy over the selection of core ML frameworks, toolchains, API platforms, and the prioritization of the AI/ML Engineering backlog.
Build vs. Buy Strategy: Owns the definitive recommendation and roadmap for multi-million dollar technology investments—including fine-tuning internal proprietary solutions versus integrating external vendor ecosystems.