AI Commercial & ML Ops Engineer

Sourced from flexgen.zya.me

This senior-level MLOps role focuses on designing, building, and operating scalable machine learning and AI deployment pipelines across cloud platforms. The engineer will automate the full ML lifecycle, including data ingestion, training, deployment, monitoring, and retraining, while implementing CI/CD, governance, and security best practices. This individual contributor position collaborates with data science, data engineering, and analytics teams, and mentors others on MLOps standards.

• Design, build, and operate end-to-end ML/AI pipelines for batch, streaming, and real-time inference use cases.
• Automate the full ML lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining.
• Implement CI/CD pipelines for ML systems with automated testing, validation gates, and controlled model promotion.
• Develop orchestration workflows using tools such as Airflow, Kubeflow, and MLflow.
• Optimize AI workloads for performance, scalability, and cost efficiency using distributed compute and cloud-native services.
• Establish monitoring, observability, drift detection, and bias monitoring frameworks.
• Implement access controls, secrets management, compliance standards, and security best practices.
• Mentor engineers and data scientists on MLOps best practices and influence enterprise-wide architectural standards.