GenAI & ML Systems Engineer (Hybrid)

Verizon Communications; Irving (TX); Hybrid; USD 77,000 - 129,000; Full time

Benefits offered by this job

401(k) matching

Paid holidays

Personal days

Parental leave

Tuition assistance

Job summary

Verizon is seeking an AI/ML Engineer (MTS II) to advance enterprise GenAI and ML solutions. You will collaborate with Enterprise Architecture, Data Science, and Data Engineering teams to build, deploy, and govern scalable machine learning systems across real-time and batch environments.

The role emphasizes end-to-end design, automated evaluations, production observability, and cost-conscious cloud performance across GCP Vertex AI, AWS Bedrock, and self-hosted models, in a hybrid work setting.

Qualifications

• Bachelor’s degree or higher with relevant work experience in ML/AI engineering or software architecture.
• Experience in ML engineering, data engineering, or software architecture roles.
• Hands-on ML engineering techniques and tools for real-time and batch processing.
• Proficiency with cloud platforms (GCP preferred, or AWS) and modern ML lifecycles.
• Ability to design agentic workflows, retrieval-augmented generation, and governance.

Responsibilities

• Design end-to-end GenAI & ML solutions for enterprise applications.
• Build automated evaluation pipelines to measure model accuracy and latency.
• Implement production observability and governance to ensure security and uptime.
• Optimize cloud and model performance across platforms (GCP, AWS) and self-hosted models.

Skills

GenAI & ML design

Governance pipelines

Production observability

Cloud & model optimization

Education

Bachelor’s degree

Tools

Docker

Kubernetes

Jenkins

GitLab CI

Python

FastAPI

PySpark

SQL

Kafka/Pulsar

Job description

Verizon is seeking an AI/ML Engineer (MTS II) to advance enterprise GenAI and ML solutions. You will collaborate with Enterprise Architecture, Data Science, and Data Engineering teams to build, deploy, and govern scalable machine learning systems across real-time and batch environments.

The role emphasizes end-to-end design, automated evaluations, production observability, and cost-conscious cloud performance across GCP Vertex AI, AWS Bedrock, and self-hosted models, in a hybrid work setting.