GenAI & Agentic AI Engineer — Hybrid, Production-Ready

Verizon; Irving (TX); Hybrid; USD 81,000 - 155,000; Full time

Benefits offered by this job

Medical, dental, vision

Matched 401(k) savings plan

Up to 8 company paid holidays per year

Up to 6 personal days per year

Paid parental leave

Tuition assistance

Job summary

Verizon is seeking an AI Engineer to design, build, and deploy LLM-powered agents and multi-agent workflows that automate business tasks across the Verizon Business Group.

You will create retrieval-augmented generation pipelines, integrate tools and APIs, evaluate accuracy and latency, and collaborate with stakeholders to translate requirements into production-grade GenAI solutions.

Qualifications

• Bachelor’s degree or 4+ years of relevant work experience.
• ≥2 years hands-on ML/AI engineering experience including Generative AI or LLM projects.
• ≥2 years Python and SQL experience.
• Experience building LLM applications with prompting, RAG, and tool calls.
• Experience in cloud environments (GCP/AWS/Azure).
• Strong math/stats background.

Responsibilities

• Design, build, and deploy LLM-powered agents and multi-agent workflows.
• Create retrieval-augmented generation pipelines with tool and API integrations.
• Evaluate agent accuracy, response quality, and latency with guardrails and monitoring.
• Support deployment in real-time and batch environments; track performance and drift.
• Collaborate with stakeholders to translate requirements into production-grade GenAI solutions.
• Research and test new LLMs, agent frameworks, and GenAI methods.

Skills

Python

SQL

Machine Learning

Generative AI

LLM projects

Cloud computing

Education

Bachelor's degree

Tools

LangGraph

LangChain

Google ADK

MCP

RAG

LLMs

CrewAI

AutoGen

GCP

AWS

Azure

pgvector

Pinecone

Elasticsearch

Embedding models

Job description

Verizon is seeking an AI Engineer to design, build, and deploy LLM-powered agents and multi-agent workflows that automate business tasks across the Verizon Business Group.

You will create retrieval-augmented generation pipelines, integrate tools and APIs, evaluate accuracy and latency, and collaborate with stakeholders to translate requirements into production-grade GenAI solutions.