MLOps Engineer

As an MLOps Engineer at our company, you will play a crucial role in building, deploying, and maintaining machine learning models in production using Google Cloud Platform (GCP). You will collaborate closely with data scientists and engineers to ensure the reliability, scalability, and performance of our ML systems. Your expertise in software engineering, data engineering, and machine learning will be utilized to automate ML pipelines, monitor model performance, and troubleshoot any issues that arise. This is an exciting opportunity to work on cutting-edge ML projects and have a significant impact on our business. Key Responsibilities: - Design, develop, and maintain scalable and reliable MLOps pipelines on GCP - Automate the deployment and monitoring of machine learning models in production - Collaborate with data scientists to productionize their models and experiments - Implement CI/CD pipelines for machine learning models - Monitor model performance and identify areas for improvement - Troubleshoot and resolve issues related to ML infrastructure and deployments - Optimize ML pipelines for performance and cost efficiency - Develop and maintain documentation for MLOps processes and infrastructure - Stay up-to-date with the latest MLOps tools and techniques - Ensure compliance with security and data privacy regulations Required Skills & Qualifications: - Bachelor's or Master's degree in Computer Science, Engineering, or a related field - 3+ years of experience in MLOps or a related role - Strong proficiency in Python and PySpark - Extensive experience with Google Cloud Platform (GCP) services, including BigQuery, Airflow, and Dataproc - Experience with containerization technologies such as Docker and Kubernetes - Experience with CI/CD pipelines and automation tools - Solid understanding of machine learning concepts and algorithms - Experience with model monitoring and alerting tools - Excellent problem-solving and communication skills - Ability to work independently and as part of a team - Experience with infrastructure-as-code tools such as Terraform or CloudFormation is a plus (Note: Additional Information section is omitted as it is not provided in the job description),