We are looking for a highly motivated MLOps Engineer with a robust passion for automation, modern DevOps/MLOps practices, and scalable machine learning operations. The ideal candidate will have expertise in cloud-based data platforms—preferably Google Cloud Platform (GCP)—as well as experience building and managing end-to-end ML pipelines and infrastructure-as-code solutions.
Key Responsibilities & Requirements
- Demonstrated passion for automation and MLOps/DevOps methodologies.
- Strong understanding of containerization and orchestration technologies.
- Solid foundation in Machine Learning and Data Science concepts.
- Experience building and operating cloud-native data architectures, ideally leveraging GCP services.
- Hands-on experience with Infrastructure as Code (IaC) using Terraform.
- Proven understanding of CI/CD pipelines and automation tools such as Azure DevOps.
- Proficient in Python for developing automation workflows and ML components.
- Experience working within Agile development teams.
- Proficient with Git for version control and collaborative development.
- Extensive experience with GCP Vertex AI components including Feature Store, Pipelines, Notebooks, Model Registry, Endpoints, and Monitoring (or equivalent platforms).
- Strong problem-solving mindset with high levels of proactivity, analytical thinking, adaptability, and accountability.
- Excellent communication, collaboration, coaching, and mentoring skills with the ability to influence and drive technical improvements.
- Demonstrated initiative, innovation, and strategic awareness.