Senior MLOps Engineer

About the job About us: Talent has no borders. Proxify's mission is to connect top developers around the world with the opportunities they deserve. So, it doesn't matter where you are; we are here to help you fast-track your independent career in the right direction. 🙂 Since our launch, Proxify's developers have successfully worked with 1200+ happy clients to build their products and growth features. 5000+ talented developers trust Proxify and its network to fulfill their dreams and objectives. Proxify is shaped by a global network of supportive, talented developers interested in remote full-time jobs. Our Glassdoor (4.5/5) and Trustpilot (4.8/5) ratings reflect the trust developers place in us and our commitment to our members' success. The Role: We are looking for a Senior MLOps Engineer for one of our clients. You are a perfect candidate if you are growth-oriented, you love what you do, and you enjoy working on new ideas to develop exciting products. What we’re looking for: Minimum of 5 years of professional experience in MLOps or a related field. Proven experience deploying and managing machine learning models in production environments. Proficiency in scripting languages (e.g., Python) and relevant MLOps tools (e.g., TensorFlow Extended, Kubeflow, MLflow). Experience with containerization technologies (Docker) and orchestration tools (Kubernetes). Strong knowledge of cloud platforms (AWS, GCP, or Azure) and their machine learning services. Demonstrated experience implementing automated testing, validation, and deployment processes for machine learning models. Located in CET timezone (+/- 3 hours), we are unable to consider applications from candidates in other time zones. Must-have skills: Python Azure / AWS / GCP Grafana / Prometheus SQL Responsibilities: Develop and implement a comprehensive MLOps strategy, ensuring the seamless integration of machine learning models into our production environment. Design, build, and maintain end-to-end machine learning pipelines, encompassing data preprocessing, model training, deployment, and monitoring. Collaborate with cross-functional teams to design, deploy, and manage scalable infrastructure for machine learning workloads. Utilise containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, GCP, or Azure). Implement and manage CI/CD pipelines for machine learning models, enabling automated testing, validation, and deployment. Establish robust monitoring and logging systems to track the performance of machine learning models in production, ensuring timely detection of anomalies and potential issues. Work closely with data scientists, software engineers, and other stakeholders to understand model requirements, deployment needs, and data dependencies. Implement security best practices for machine learning systems and ensure compliance with relevant regulations and standards. What we offer: Get paid, not played No more unreliable clients. Enjoy on-time monthly payments with flexible withdrawal options Predictable project hours Enjoy a harmonious work-life balance with consistent 8-hour working days with clients. Flex days, so you can recharge Enjoy up to 24 flex days off per year without losing pay, for full-time positions found through Proxify. Career-accelerating positions at cutting-edge companies Discover exclusive long-term remote positions at the world's most exciting companies. Hand-picked opportunities just for you Skip the typical recruitment roadblocks and biases with personally matched positions. One seamless process, multiple opportunities A one-time contracting process for endless opportunities, with no extra assessments. Compensation Enjoy the same pay, every month with positions landed through Proxify.