MLOps Engineer

Role Summary As an MLOps Engineer, you will be leveraging your expertise in machine learning and operations to design and implement robust ML pipelines. Your day-to-day will involve integrating generative AI solutions, optimizing workflows on Google Cloud Platform, and collaborating with data scientists to ensure smooth deployment and scaling of models. Responsibilities - Develop and maintain end-to-end machine learning workflows using tools like Kubeflow and Vertex AI. - Implement MLOps practices to automate the deployment, monitoring, and scaling of machine learning models in production. - Utilize BigQuery for data analysis and feature engineering to support model building. - Create Docker containers for applications and manage Kubernetes clusters for orchestration of services. - Collaborate with cross-functional teams to understand requirements and translate them into efficient technological solutions. - Use Terraform for infrastructure as code (IaC) to manage cloud resources efficiently. Must-Have Skills - Strong experience in MLOps frameworks and practices. - Proficiency in generative AI applications and methodologies. - Solid background as a machine learning engineer with hands-on experience on GCP (Google Cloud Platform). - Expertise in Vertex AI, Kubeflow, BigQuery, Python, Docker, Kubernetes, and Terraform. Good-to-Have Skills - Familiarity with CI/CD processes specific to ML deployments. - Experience working on large-scale distributed systems or big data technologies. - Understanding of model governance and compliance in machine learning. Ready to take your career to the next level Apply now!