Senior AI Engineer - Live Operations

When you join Verizon

You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife.

What you’ll be doing…

As a Senior AI/ML Engineer - AI Live Operations, you will apply your advanced technical expertise in data science, system orchestration, and software engineering to maintain the stability, safety, and efficiency of our deployed artificial intelligence models. You will be a key individual contributor within the Live Operations team, taking responsibility for the health of machine learning systems once they are exposed to real-world datasets and production traffic.

At Verizon, we are on a multi-year journey to industrialize our data science and AI capabilities. Very simply, this means that AI will fuel decisions and business processes across the company. At over one hundred thirty billion dollars in annual revenue, this is a pioneering opportunity to build and scale production AI systems in the telecommunications industry. With our leadership in bringing 5G network nationwide, you will help us transition from millions of automated predictions to trillions of real-time inferences.

• Building and maintaining real-time monitoring and observability pipelines to track system throughput, latency, and token consumption costs for large language models.
• Monitoring production model behavior to identify data drift and model degradation, ensuring our systems maintain high predictive accuracy over time.
• Implementing automated continuous evaluation loops that capture user feedback and ground-truth telemetry to dynamically assess performance.
• Deploying real-time safety filters, system guardrails, and input/output moderators to mitigate model hallucinations and prevent inappropriate content generation.
• Integrating robust fallback systems, including routing failures to traditional rule-based software systems or human queues, to preserve end-user experience.
• Developing automated continuous integration and continuous deployment pipelines to retrain, validate, and seamlessly release updated models without downtime.
• Resolving production model incidents as a senior escalation contact, diagnosing issues with live models, and shipping immediate hotfixes or prompt adjustments.
• Collaborating with platform architects and core data science teams to scale backend architectures across cloud, on-premises, and edge network infrastructures.
• Partnering with peer engineering and development teams to foster technical alignment, conduct code reviews, and champion secure, ethical AI standards.

Where you’ll be working…

This hybrid role will have a defined work location that includes work from home and assigned office days as set by the manager.

What we’re looking for…

You are a highly skilled engineer who excels in the intersection of data science, software development, and operations. You understand that machine learning models are not static, and you thrive on solving the complex, real-time challenges that occur once algorithms meet actual users. You are a self-starter who values precision, system resilience, and clean, scalable code. You enjoy mentoring junior team members and sharing technical knowledge across the larger engineering community.

You’ll need to have:

• Bachelor’s degree or four or more years of work experience.
• Four or more years of relevant experience required, demonstrated through work experience and/or military experience.

Even better if you have one or more of the following:

• Master’s degree in computer science, data science, electrical engineering, mathematics, or another highly technical discipline.
• Extensive hands-on experience deploying, monitoring, and debugging complex machine learning or deep learning models in large-scale production environments.
• Practical familiarity with MLOps frameworks and automated pipeline tools, including Airflow, Kubeflow, or MLflow.
• Advanced programming expertise in Python, Java, o…