Engineer I-Systems Engineering

Jobverse.io; Florida; On-site; USD 70,000 - 110,000; Full time

Job summary

Verizon is seeking an Engineer I - Systems Engineering to build and deploy generative AI and agentic systems for operational tools. The role focuses on multi-agent workflows, graph-driven data pipelines, APIs and voice AI features, machine-learning models, and production deployment.

You will architect and implement stateful AI workflows, integrate graph databases like Neo4j with Pandas data wrangling, and deliver production-grade microservices.

Qualifications

• Experience designing and deploying AI-powered workflows.
• Hands-on with CI/CD and MLOps practices.
• Proficiency in Python and Node.js for backend services.

Responsibilities

• Architect and build stateful, multi-agent AI workflows.
• Design data pipelines linking graph databases with search and caching layers.
• Develop APIs and voice AI features for mobile apps.
• Train and refine ML models with PyTorch and TensorFlow.
• Automate build and deployment pipelines using Jenkins and Docker.

Skills

LangGraph

LangChain

Graph databases

Neo4j

Python

Node.js

PyTorch

TensorFlow

MLOps

Docker

Jenkins

APIs

Tools

Docker

Jenkins

Job description

Verizon is hiring an Engineer I - Systems Engineering to build and deploy generative AI and agentic systems for operational tools. The role focuses on multi-agent workflows, graph-driven data pipelines, APIs and voice AI features, machine-learning models, and production deployment.

Responsibilities

• Engineering Agentic Systems: Architecting, building, and maintaining stateful, multi-agent AI workflows using LangGraph and LangChain to automate complex decision-making processes
• Building Graph & Data Pipelines: 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
• Developing APIs & Voice AI Features: 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
• Building & Optimizing ML Models: Leveraging PyTorch and TensorFlow to build, fine-tune, and evaluate machine learning models for natural language processing and decision flows
• Managing CI/CD & MLOps: Automate build and deployment pipelines using Jenkins alongside Docker containerization to deploy scalable microservices
• Delivering Enterprise Solutions: Write production-grade code, participate in code reviews, and collaborate with team members to turn prototype AI models into reliable, production-grade applications