Principal AI Engineer

Verizon Verizon · Telecom · Basking Ridge, NJ +2

Principal AI Engineer to lead the technical vision, architectural design, and execution of cutting-edge agentic AI/ML solutions within the Verizon Consumer Group (VCG) AI team. The role involves building intelligent, autonomous agents to power digital channels, sales, and customer success ecosystems, translating strategy into scalable and secure production systems, and developing a technical roadmap for agentic systems.

What you'd actually do

  1. Driving the technical strategy, research, and application of advanced agentic AI, large language models (LLMs), and cognitive frameworks to solve complex consumer challenges.
  2. Designing and implementing production-grade, highly scalable AI/ML pipelines and agent architectures that integrate seamlessly with digital and customer success platforms.
  3. Collaborating with Global Technology Architecture, Security, and Legal teams to ensure all agentic systems conform to the highest standards of data privacy, compliance, and engineering rigor.
  4. Partnering with Customer Service Operations and VCG Strategy teams to identify high-impact business applications and translate them into robust technical designs.
  5. Developing and maintaining a three-to-five-year technical roadmap for agentic systems across VCG, outlining the future state of the technology stack.

Skills

Required

  • Designing, building, and deploying production AI/ML systems at scale
  • software development methodologies (e.g., Agile)
  • technical strategy
  • architectural design
  • execution of cutting-edge agentic AI/ML solutions
  • large language models (LLMs)
  • cognitive frameworks
  • production-grade, highly scalable AI/ML pipelines
  • agent architectures
  • data privacy
  • compliance
  • engineering rigor
  • technical roadmap development
  • mentoring engineers
  • engineering best practices
  • red-teaming methodologies

Nice to have

  • Master's degree or PhD in Computer Science, Data Science, or a related quantitative field
  • modern LLM application frameworks (e.g., LangChain, LlamaIndex)
  • vector databases
  • API design and development
  • cloud platforms (e.g., GCP, AWS, Azure)
  • cloud-native technologies
  • lead technical planning, prioritization, and large-scale projects

What the JD emphasized

  • production AI/ML systems at scale
  • agentic AI/ML solutions
  • intelligent, autonomous agents
  • agentic systems
  • agentic architectures

Other signals

  • building intelligent, autonomous agents
  • designing and implementing production-grade, highly scalable AI/ML pipelines and agent architectures
  • developing and maintaining a three-to-five-year technical roadmap for agentic systems