Description

Lead technical teams in managing the end-to-end lifecycle of machine learning models and production infrastructure.

Responsibilities

• Oversee deployment of machine learning models into production environments and ensure continuous performance monitoring.
• Implement CI/CD pipelines for seamless model updates and rollbacks.
• Design and manage scalable infrastructure for model training and deployment.
• Automate repetitive tasks to improve efficiency and reduce errors across ML workflows.
• Collaborate with cross-functional teams to translate business requirements into technical solutions and provide guidance to team members.

Required Skills

• 14+ years of experience across DevOps, MLOps, and data engineering domains.
• Proven experience managing and leading technical teams.
• Proficiency in Python and R.
• Experience with MLOps tools such as Kubeflow, MLflow, and Airflow.
• Familiarity with cloud platforms including AWS, GCP, and Azure.
• Knowledge of container orchestration with Kubernetes.
• Strong understanding of CI/CD, Infrastructure as Code (IaC), and monitoring.
• Expertise in data pipelines, data cleaning, and feature engineering.
• Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.

Preferred Skills

• Experience with big data technologies including Hadoop and Spark.
• Knowledge of data governance and security practices.

Key Skills

Education

Bachelor’s or master’s degree in computer science, Engineering