AI Commercial, ML Ops Engineer

Role Overview

• Design, build, and operate end-to-end machine learning and artificial intelligence pipelines supporting batch, streaming, and real-time inference use cases
• Automate the full machine learning lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining
• Implement CI/CD pipelines for machine learning systems with automated testing, validation gates, and controlled model promotion
• Develop orchestration workflows using tools such as Airflow, Kubeflow, and MLflow for experiment tracking and governance
• Optimize artificial intelligence workloads for performance, scalability, and cost efficiency using distributed compute and cloud-native services
• Establish monitoring and observability frameworks including performance metrics, data quality checks, drift detection, and bias monitoring
• Design automated retraining strategies including trigger-based, schedule-based, and performance-based refresh cycles
• Create repeatable prompting frameworks and artificial intelligence guardrails to support safe and effective AI-assisted development
• Implement access controls, secrets management, compliance standards, and security best practices across machine learning and artificial intelligence platforms
• Evaluate and operationalize emerging artificial intelligence technologies and vendor tools, identifying measurable business value
• Mentor engineers and data scientists on Machine Learning Operations best practices and influence enterprise-wide architectural standards

Requirements

• 5+ years of prior relevant experience in machine learning or artificial intelligence engineering, Data Science, or Analytics Engineering
• Bachelor’s degree in Computer Science, Data Engineering, or related field required
• Deep experience with machine learning and artificial intelligence pipeline development and full lifecycle automation
• Demonstrated ability to provide technical leadership in Machine Learning Operations strategies and pipeline standardization
• Proven impact on improving reliability, scalability, and efficiency of machine learning and artificial intelligence solutions
• Strong proficiency in Python and PySpark
• Experience designing and implementing CI/CD for machine learning workflows, including version control systems such as Git and DVC
• Experience with monitoring, logging, drift detection, and automated retraining frameworks
• Experience with cloud platforms such as Databricks, AWS, GCP, or Azure
• Proficiency in containerization and orchestration including Docker and Kubernetes
• Experience with orchestration and lifecycle management tools such as Airflow, Kubeflow, MLflow, or similar platforms
• Experience guiding teams on artificial intelligence automation best practices
• Experience supporting marketing, revenue, or operations analytics teams preferred
• Familiarity with TensorFlow, PyTorch, Scikit-learn, or similar machine learning frameworks preferred.

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• Kubernetes
• PySpark
• Python
• PyTorch
• Scikit-Learn
• Tensorflow

Benefits

• Prioritize your wellness, access programs crafted to nurture your mental and physical health.
• Enjoy unbeatable discounts on hotel stays, dining, retail, entertainment, and exclusive partner perks for travel, tech, and beyond!
• Savor delicious meals for free in our employee dining room.
• Park with ease—whether you're on or off shift, it's free!
• From healthcare to financial support and generous time-off options, we’ve got you covered.
• Elevate your career with development programs, connect through networking events, and make a difference with community volunteer opportunities.