Agentic AI Engineer – Multi-Agent Systems & LLMs

Verizon; Irving (TX); Hybrid; USD 101,000 - 194,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Verizon is seeking an Agentic AI Engineer to architect autonomous, multi-agent systems that interface with enterprise data, APIs, and memory layers. You will design stateful workflows, enable secure integration with databases, and deploy microservices with robust guardrails.

You will work on LLM-driven architectures, RAG retrieval, and governance to prevent issues while delivering scalable, customer-centric AI solutions. Hybrid work with defined on-site days is included.

Qualifications

• Bachelor’s degree or 4+ years of work experience demonstrated by roles or training.
• Experience building multi-agent systems with delegation models including human-in-the-loop.
• Hands-on in enterprise-grade agentic workflows and topologies using listed platforms.
• Experience with LLMs, prompt engineering, RAG, and guardrails.
• Familiarity with NLP, AI integration, and search relevance in enterprise tools.

Responsibilities

• Architect stateful, multi-step agentic workflows using reasoning frameworks.
• Build reliable execution layers connecting LLMs to external systems via REST APIs and connectors.
• Implement memory architectures, state persistance, and context window optimization.
• Establish offline and real-time guardrails to prevent hallucinations and drift.
• Containerize and deploy multi-agent microservices with scalable patterns.

Skills

Multi-Agent Systems

LLMs & Prompting

RAG architectures

NLP integration

Guardrails & safety

Education

Bachelor’s degree or 4+ years experience

Tools

LangGraph

Google ADK / CES

Sierra

Decagon

REST APIs

Python

Java

NodeJS

Job description

Verizon is seeking an Agentic AI Engineer to architect autonomous, multi-agent systems that interface with enterprise data, APIs, and memory layers. You will design stateful workflows, enable secure integration with databases, and deploy microservices with robust guardrails.

You will work on LLM-driven architectures, RAG retrieval, and governance to prevent issues while delivering scalable, customer-centric AI solutions. Hybrid work with defined on-site days is included.