Description

You will own the end-to-end lifecycle of machine learning models and data processing workflows within cloud environments.

Responsibilities

• Design and maintain scalable ML pipelines and infrastructure on GCP and AWS.
• Automate data ingestion, processing, and model deployment using Python and PySpark.
• Build and manage CI/CD pipelines for continuous integration and delivery of ML models.
• Develop monitoring and logging systems to ensure reliability and performance of deployed models.
• Deploy models into production while ensuring security and compliance standards.

Required Skills

• 5+ years of professional experience in MLOps, Data Engineering, or a related role.
• Strong proficiency in Python and PySpark for data processing and model development.
• Hands-on experience with cloud platforms, specifically AWS and GCP.
• Experience with containerization and orchestration using Docker and Kubernetes.
• Proficiency with CI/CD tools such as Jenkins or GitLab CI/CD.
• Experience with infrastructure-as-code tools like Terraform or CloudFormation.
• Knowledge of model monitoring and logging tools such as Prometheus or Grafana.
• Bachelor's Degree in Computer Science, Data Science, or a related field.

Preferred Skills

• Experience with big data tools including Apache Kafka, Hadoop, or Spark.
• Familiarity with data science libraries such as TensorFlow, Scikit-learn, or PyTorch.

Key Skills

Education

Bachelor's Degree