Senior AI Engineer – Live Operations

Verizon

Senior AI/ML Engineer maintaining deployed AI model efficiency, collaborating across teams for Verizon technology initiatives. Key contributor in the Live Operations team tackling real-world data challenges.

Posted 7/22/2026full-timeBasking Ridge • New Jersey, Texas • 🇺🇸 United StatesSenior💰 $101,000 - $194,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates advanced technical expertise in data science, system orchestration, and software engineering, with a focus on deploying and monitoring machine learning models in production environments. Proficient in implementing MLOps frameworks and developing safety guardrails for AI applications.

Highest-signal resume keywords

Machine Learning Model DeploymentMLOps FrameworksPython ProgrammingCloud Systems (AWS, Google Cloud, Azure)Safety Guardrail Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceSoftware EngineeringModel MonitoringAutomated Pipeline DevelopmentDebugging Complex ModelsData StructuresDistributed Compute ArchitecturesDatabase DesignReal-Time Streaming TechnologiesTelemetry Systems

Tools & Technologies

AirflowKubeflowMLflowKafkaSpark

Industry Keywords

AI ModelsData DriftModel DegradationContinuous IntegrationContinuous Deployment

Tech Stack

Tools & technologies

AirflowAWSAzureCloudJavaKafkaPythonSpark

About the role

Key responsibilities & impact

Apply advanced technical expertise in data science, system orchestration, and software engineering to maintain the stability, safety, and efficiency of deployed AI models
Monitor production model behavior to identify data drift and model degradation, ensuring high predictive accuracy over time
Implement automated continuous evaluation loops that capture user feedback to dynamically assess performance
Deploy real-time safety filters, system guardrails, and input/output moderators to mitigate model hallucinations
Integrate robust fallback systems to preserve end-user experience
Develop automated continuous integration and deployment pipelines to retrain, validate, and release updated models
Resolve production model incidents as a senior escalation contact, diagnosing issues with live models
Collaborate with platform architects and core data science teams to scale backend architectures across cloud, on-premises, and edge network infrastructures

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++, with a 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