Principal Machine Learning Architect

Role Overview: At Collinson, as a Machine Learning (ML) Engineer, you will play a critical role in driving the development of cloud-based machine learning pipelines for data-driven products and services. Your responsibilities will include collecting data from various business units and leveraging a centralized data platform to productionize analytics and machine learning workflows. Additionally, you will be expected to provide analytical expertise across the Collinson group, ensuring the implementation of cloud-based solutions that meet the needs of both internal and external clients from across Collinson's global footprint. As an innovator, you will be tasked with bringing fresh ideas to the team and continuously exploring new and modern engineering frameworks to enhance the overall offerings of the Collinson group. A key aspect of this role will also be to collaborate with the data platform team to integrate with the ML platform and to support the growth and development of the team's ML skillset. It will be essential for you to be able to identify and resolve issues that arise, ensuring the quality and quantity of work produced by the team is always maximized. This will require a combination of technical expertise, problem-solving skills, and the ability to effectively communicate and collaborate with stakeholders. Key Responsibilities: - Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms. - Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies. - Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models. - Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems. - Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms. - Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization. Qualification Required: - Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related fields. A Ph.D. is advantageous. - 7+ years of proven hands-on experience building ML/AI platforms, especially involving AWS SageMaker and distributed computing frameworks. Additional Details of the Company: Collinson is a global, privately-owned company dedicated to helping the world to travel with ease and confidence. They craft customer experiences that enable some of the worlds best-known brands to acquire, engage, and retain demanding customers. With over 30 years of experience and 2,000 passionate people working in 20 locations worldwide, Collinson offers solutions such as Priority Pass, the worlds best-known airport experiences program, and is a trusted partner behind many leading financial services, airline, and hotel brands' reward programs and loyalty initiatives. Role Overview: At Collinson, as a Machine Learning (ML) Engineer, you will play a critical role in driving the development of cloud-based machine learning pipelines for data-driven products and services. Your responsibilities will include collecting data from various business units and leveraging a centralized data platform to productionize analytics and machine learning workflows. Additionally, you will be expected to provide analytical expertise across the Collinson group, ensuring the implementation of cloud-based solutions that meet the needs of both internal and external clients from across Collinson's global footprint. As an innovator, you will be tasked with bringing fresh ideas to the team and continuously exploring new and modern engineering frameworks to enhance the overall offerings of the Collinson group. A key aspect of this role will also be to collaborate with the data platform team to integrate with the ML platform and to support the growth and development of the team's ML skillset. It will be essential for you to be able to identify and resolve issues that arise, ensuring the quality and quantity of work produced by the team is always maximized. This will require a combination of technical expertise, problem-solving skills, and the ability to effectively communicate and collaborate with stakeholders. Key Responsibilities: - Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms. - Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies. - Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models. - Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems. - Ensure best practices

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