Lead Machine Learning Engineer

About The Company
Aioi Nissay Dowa Europe is a leading insurance provider specializing in connected mobility protection. As one of the fastest-growing companies in the industry, we are dedicated to innovating and delivering comprehensive insurance solutions that meet the evolving needs of our customers. Our commitment to excellence, innovation, and customer satisfaction has positioned us as a trusted name in the insurance sector. We foster a dynamic and inclusive work environment that values diversity, teamwork, and continuous learning. Our recognition as the Best Large Insurance Employer at the 2023 British Insurance Awards underscores our dedication to creating a supportive and rewarding workplace for our employees.
About The Role
We are seeking a highly skilled and experienced Lead Machine Learning Engineer to join our team at Aioi Nissay Dowa Europe. This pivotal role combines technical leadership, project delivery, and team management responsibilities within our R&D Lab. As a Lead ML Engineer, you will play a crucial role in shaping our machine learning engineering practices, supporting the design and deployment of scalable ML systems, and mentoring junior engineers. You will act as both a hands-on contributor and a strategic leader, overseeing multiple projects simultaneously while ensuring high-quality output and adherence to best practices. Your expertise will support use case discovery, solution design, and pre-sales activities, helping to identify new opportunities for AI and ML applications. This role offers flexibility in working arrangements, including hybrid or remote options, to promote work-life balance and accommodate your preferences.
Qualifications
The ideal candidate will possess a strong academic background in a relevant STEM discipline, such as a PhD or MSc in Computer Science, Mathematics, Statistics, Engineering, or Physics, or equivalent practical experience. Extensive experience in machine learning engineering, software development, or related technical roles is essential, particularly in delivering production-grade ML systems and leading technical teams. A solid software engineering foundation with experience in building and maintaining data-driven systems in cloud environments is required. Proven leadership skills in managing ML engineers or technical contributors, supporting pre-sales activities, and guiding solution design are highly valued. Excellent communication skills are necessary to articulate technical decisions and risks effectively to both technical and non-technical stakeholders. Familiarity with cloud platforms, CI/CD pipelines, containerization, and operational support for ML systems is also important. Candidates should demonstrate strong problem-solving abilities, adaptability in fast-paced environments, and a track record of hands-on delivery alongside leadership responsibilities.
Responsibilities
Line manage and develop ML engineers through coaching, mentoring, performance reviews, and supporting their professional growth.
Contribute actively to the technical delivery of selected projects, ensuring high standards of quality and innovation.
Collaborate with senior technical leaders to establish and embed best practices in ML engineering across the organization.
Lead multidisciplinary teams comprising ML engineers, scientists, and software engineers to ensure effective project execution and continuous improvement.
Mentor junior engineers, supporting their technical development and fostering a culture of learning and excellence.
Support solution design by providing technical guidance to ensure ML systems are robust, scalable, and maintainable.
Oversee multiple concurrent projects, managing technical and delivery risks, and ensuring alignment with organizational goals.
Engage directly in complex or high-priority technical delivery tasks when required, providing hands-on expertise.
Lead use case discovery efforts, working with stakeholders to identify AI and ML opportunities that align with business objectives.
Assist in pre-sales activities by shaping solutions and providing technical insights to clients and internal teams.
Partner with project management teams to plan, scope, and support the successful delivery of ML initiatives.
Oversee the maintenance and continuous improvement of deployed ML solutions, ensuring they meet evolving requirements.
Promote the reuse of shared frameworks, standards, and tooling to enhance efficiency across projects.
Communicate complex technical decisions, risks, and trade-offs clearly to non-technical audiences, ensuring transparency and understanding.
Essential
Knowledge, Experience and Qualifications
PhD or MSc in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related STEM field, or equivalent practical experience.
Extensive experience in machine learning engineering, software development, or related technical roles, with a proven track record of delivering production ML systems.
Strong software engineering skills, including building and deploying data-driven systems in cloud environments.
Experience leading or managing ML engineers or technical teams in a delivery-focused setting.
Supportive experience in pre-sales activities, technical discovery, and opportunity shaping in collaboration with commercial teams.
Ability to provide technical leadership across solution design, deployment, and ongoing maintenance of ML systems.
Experience managing the full lifecycle of ML solutions, from use case discovery to deployment and continuous improvement.
Deep understanding of engineering best practices, including code quality, testing, documentation, and maintainability.
Proficiency working within multidisciplinary teams, including ML engineers, scientists, software engineers, and stakeholders.
Hands-on experience with cloud platforms, CI/CD pipelines, containerization, and operational support for ML systems.
Excellent communication skills, capable of translating complex technical concepts for diverse audiences.
Strong problem-solving skills, with the ability to prioritize and make sound judgments in dynamic environments.
Desirable
Experience establishing or embedding best practices in ML engineering, MLOps, or

Work arrangement
Yes