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.