Senior AI Engineer – Live Operations

• 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

• 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.

Core Competencies

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 Deployment
• MLOps Frameworks
• Python Programming
• Cloud Systems (AWS, Google Cloud, Azure)
• Safety Guardrail Development

ATS Optimization Keywords

Hard Skills

• Data Science
• Software Engineering
• Model Monitoring
• Automated Pipeline Development
• Debugging Complex Models
• Data Structures
• Distributed Compute Architectures
• Database Design
• Real-time Streaming Technologies
• Telemetry Systems

Industry Keywords

• AI Models
• Data Drift
• Model Degradation
• Continuous Integration
• Continuous Deployment

Tools & Technologies

• Airflow
• Kubeflow
• MLflow
• Kafka
• Spark