team will be responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production. Acting as the critical link between the datascience and R&D teams, this team will ensure that ML models transition smoothly from development to production, maintaining high availability, scalability, and … deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing … and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of data engineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems. Strong communication and collaboration skills. More ❯
tangible impact. You will be involved in the full software development lifecycle, from requirements gathering to deployment and maintenance, working closely with hardware engineers, data scientists, and aquaculture specialists. Responsibilities Design, develop, and debug embedded software and firmware for a variety of aquaculture monitoring and control systems (e.g., sensor … systems. Work with microcontrollers, microprocessors, and real-time operating systems (RTOS). Implement communication protocols (e.g., MQTT, UART, SPI, I2C, Ethernet, Bluetooth, LoRaWAN) for data transmission and device control. Integrate software with various hardware components, including sensors, actuators, and communication modules. Participate in the full software development lifecycle, including … and system tests to ensure software quality and reliability. Troubleshoot and debug software and hardware issues in embedded systems. Collaborate effectively with hardware engineers, data scientists, and aquaculture experts to define system requirements and ensure seamless integration. Contribute to the continuous improvement of our software development processes and tools. More ❯
deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing … and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of data engineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems.. If you have the skills and desire for this, then More ❯
deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing … and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of data engineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems.. If you have the skills and desire for this, then More ❯
