Analyzing, designing, developing, and managing the infrastructure to release scalable Data Science models. The ML Engineer is expected to deploy, monitor and operate production grade AI systems in a scalable, automated, and repeatable way.Job DescriptionCreate and maintain a scalable infrastructure to deliver AI/ML processes, responding to the user requests in near real time.Design and implement the pipelines for training and deployment of ML models.Design dashboards to monitor a system.Collect metrics and create alerts based on them.Design and execute performance tests.Perform feasibility studies/analysis with a critical point of view.Support and maintain (troubleshoot issues with data and applications).Develop technical documentation for applications, including diagrams and manuals.Working on many different software challenges always ensuring a combination of simplicity and maintainability within the code.Contribute to architectural designs of large complexity and size, potentially involving several distinct software components.Mentoring other engineers fostering good engineering practices across the department.Working closely with data scientists and a variety of end-users (across diverse cultures) to ensure technical compatibility and user satisfaction.Work as a member of a team, encouraging team building, motivation and cultivating effective team relations.QualificationsRole Requirements E=essential, P=preferredP - Bachelor's degree in Computer Science or related field.P - Master's degree in data engineering or related.E - Demonstrated experience and knowledge in Linux and Docker containers.E - Demonstrated experience and knowledge in some of the main cloud providers (Azure, GCP or AWS).P - Demonstrated experience and knowledge in distributed systems.E - Proficient in programming languages: Python.E – Experience with ML/Ops technologies like Azure ML.E – Self-driving and good communication skills.P – Experience with AI/ML frameworks: Torch, Onnx, Tensorflow.E - Experience designing and implementing CICD pipelines for automation.P - Experience designing monitoring dashboards (Grafana or similar).P - Experience with container orchestrators (Kubernetes, Docker Swarm).E - Experience in using collaborative developing tools such as Git, Confluence, Jira, etc.E - Problem-solving capabilities.E - Strong ability to analyze and synthesize (Good analytical and logical thinking capability).E - Proactive attitude, resolutive, used to work in a team and manage deadlines.Additional InformationFlexible work model - the Team is meeting at the office 1 or 2 times a month.Flexible working environment.Volunteer time off.#J-18808-Ljbffr
