Engineer I - Systems Engineering

Verizon•Temple Terrace, FL

2d•Hybrid

About The Position

As an Engineer I - Systems Engineering (Generative AI & Agentic Systems), you will work at the forefront of enterprise AI—building autonomous agentic frameworks, graph-driven retrieval pipelines, microservices, and edge voice-activation features for critical operational tools. You will be instrumental in deploying intelligent agents that power real-time decision engines (Next Best Action) for our field operations and support teams.

Requirements

Bachelor’s degree or one or more years of relevant experience required, demonstrated through work experience and/or military experience.
Experience in software engineering, systems engineering with AI/ML development.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.
Proficiency in Python and Node.js for backend development and API integration.
Exposure to voice recognition, keyword spotting (KWS), or voice wake-up word detection systems.
Familiarity with vector search engines (Elasticsearch) and caching layers (Redis).
Practical experience building multi-agent AI systems with LangChain and LangGraph
Hands-on experience with Graph Databases (Neo4j) and data manipulation libraries like Pandas.
Experience managing CI/CD automation using Jenkins, containerizing applications with Docker, and using Git for version control.

Responsibilities

Architecting, building, and maintaining stateful, multi-agent AI workflows using LangGraph and LangChain to automate complex decision-making processes.
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.
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.
Leveraging PyTorch and TensorFlow to build, fine-tune, and evaluate machine learning models for natural language processing and decision flows.
Automating build and deployment pipelines using Jenkins alongside Docker containerization to deploy scalable microservices.
Writing production-grade code, participating in code reviews, and collaborating with team members to turn prototype AI models into reliable, production-grade applications.