Engineer I - Systems Engineering

Verizon Verizon · Telecom · Temple Terrace, FL

This role focuses on engineering agentic systems, building graph and data pipelines, developing APIs and voice AI features, building and optimizing ML models, managing CI/CD and MLOps, and delivering enterprise solutions using GenAI and agentic frameworks. The primary focus is on building multi-agent AI workflows and integrating voice AI features.

What you'd actually do

  1. Architecting, building, and maintaining stateful, multi-agent AI workflows using LangGraph and LangChain to automate complex decision-making processes.
  2. Designing and optimizing data architectures linking Graph Databases (Neo4j) with search and caching layers, using Pandas for data wrangling, feature engineering, and processing unstructured datasets.
  3. Building robust backend services and APIs using Node.js and Python to integrate edge-compatible voice wake-up word features and speech processing into mobile applications for field technicians.
  4. Leveraging PyTorch and TensorFlow to build, fine-tune, and evaluate machine learning models for natural language processing and decision flows.
  5. Automate build and deployment pipelines using Jenkins alongside Docker containerization to deploy scalable microservices.

Skills

Required

  • Bachelor’s degree or one or more years of relevant experience
  • Experience in software engineering, systems engineering with AI/ML development
  • Python
  • Node.js
  • PyTorch
  • TensorFlow
  • LangGraph
  • LangChain
  • Neo4j
  • Pandas
  • Jenkins
  • Docker
  • Git

Nice to have

  • Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field
  • voice recognition
  • keyword spotting (KWS)
  • voice wake-up word detection systems
  • vector search engines (Elasticsearch)
  • caching layers (Redis)

What the JD emphasized

  • AI/ML development
  • building multi-agent AI systems
  • voice wake-up word features
  • speech processing

Other signals

  • building autonomous agentic frameworks
  • deploying intelligent agents
  • building graph & data pipelines
  • building & optimizing ML models
  • turning prototype AI models into reliable, production-grade applications