Location
Homeoffice | Germany
Job description
Time zones: EST (UTC -5) , MST (UTC -7) , ART (UTC -3) , UTC -4 , UTC -4:30 , UTC -3 , UTC -2 , SBT (UTC +11) , GMT (UTC +0) , CET (UTC +1) , EET (UTC +2) , MSK (UTC +3) , CEST (UTC +2) , BST (UTC +1) , JST (UTC +9) , CST (UTC +8) , WIB (UTC +7) , MMT (UTC +6:30) , BST (UTC +6) , NPT (UTC +5:45) , IST (UTC +5:30) , UZT (UTC +5) , IRDT (UTC +4:30) , GST (UTC +4)
The Role:
We are looking for a Senior MLOps engineer with commercial experience for one of our clients. You are a perfect candidate if you are growth-oriented, love what you do, and enjoy working on new ideas to develop exciting products and growth features.
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.
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 Proxify offers
- Career-accelerating positions at cutting-edge companies
Discover exclusive long-term remote engagements at the world's most interesting product companies. - Hand-picked opportunities, just for you
Skip the typical recruitment roadblocks and biases with personally matched engagements. - Fast-track your independent developer career
Start small and gain more freedom to take on new engagements as you build your independent developer career. - A recruitment process that values your time
Only one hiring process with the possibility of several positions, without any additional tests.
Job tags
Salary
