Senior AI Engineer – Live Operations

Verizon

Senior AI Engineer applying advanced data science and software engineering to maintain stability of AI models at Verizon. Managing deployment and monitoring of machine learning systems in production environments.

Posted 7/31/2026full-timeBasking Ridge • New Jersey, Texas • 🇺🇸 United StatesSenior💰 $101,000 - $194,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced technical expertise in data science, machine learning, and software engineering, with a focus on deploying and monitoring AI models in production environments. Proficient in building observability pipelines and implementing safety measures to ensure model performance and user experience.

Highest-signal resume keywords

Machine Learning Model DeploymentMLOps FrameworksReal-Time MonitoringPython ProgrammingCloud Systems (AWS, Google Cloud, Azure)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningData StructuresTelemetry SystemsSafety Guardrail MiddlewareAutomated Pipeline ToolsDebuggingModel MonitoringContinuous IntegrationContinuous Deployment

Soft Skills

CollaborationTechnical AlignmentProblem Solving

Tools & Technologies

AirflowKubeflowMLflowKafkaSpark

Industry Keywords

Generative AIDistributed Compute ArchitecturesReal-Time Streaming TechnologiesCloud Infrastructure

Tech Stack

Tools & technologies

AirflowAWSAzureCloudJavaKafkaPythonSpark

About the role

Key responsibilities & impact

Apply your advanced technical expertise in data science, system orchestration, and software engineering to maintain the stability, safety, and efficiency of deployed artificial intelligence models
Be a key individual contributor within the Live Operations team, taking responsibility for the health of machine learning systems once exposed to real-world datasets and production traffic
Build and maintain real-time monitoring and observability pipelines to track system throughput, latency, and token consumption costs for large language models
Monitor production model behavior to identify data drift and model degradation, ensuring systems maintain high predictive accuracy over time
Implement automated continuous evaluation loops that capture user feedback and ground-truth telemetry to dynamically assess performance
Deploy real-time safety filters, system guardrails, and input/output moderators to mitigate model hallucinations and prevent inappropriate content generation
Integrate robust fallback systems, including routing failures to traditional rule-based software systems or human queues, to preserve end-user experience
Develop automated continuous integration and continuous deployment pipelines to retrain, validate, and seamlessly release updated models without downtime
Resolve production model incidents as a senior escalation contact, diagnosing issues with live models, and shipping immediate hotfixes or prompt adjustments
Collaborate with platform architects and core data science teams to scale backend architectures across cloud, on-premises, and edge network infrastructures
Partner with peer engineering and development teams to foster technical alignment, conduct code reviews, and champion secure, ethical AI standards

Requirements

What you’ll need

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
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, or C++
Strong grasp of data structures and cloud systems such as AWS, Google Cloud, or Azure
Deep experience developing safety guardrail middleware, system monitoring dashboards, and telemetry systems for Generative AI applications
Knowledge of distributed compute architectures, database design, and real-time streaming technologies such as Kafka or Spark

Benefits

Comp & perks

medical, dental, vision
short and long term disability
basic life insurance
supplemental life insurance
AD&D insurance
identity theft protection
pet insurance
group home & auto insurance
matched 401(k) savings plan
up to 8 company paid holidays per year
up to 6 personal days per year
paid parental leave
adoption assistance
tuition assistance
other incentives