Who are we?👋 Look at the latest headlines and you will see something Ki insures. Think space shuttles, world tours, wind farms, and even footballers’ legs. Ki’s mission is simple. Digitally disrupt and revolutionise a 335-year-old market. Working with Google and UCL, Ki has created a platform that uses algorithms, machine learning and large language models to give insurance brokers quotes in seconds, rather than days. Ki is proudly the biggest global algorithmic insurance carrier. It is the fastest growing syndicate in the Lloyd's of London market, and the first ever to make $100m in profit in 3 years. Ki’s teams have varied backgrounds and work together in an agile, cross-functional way to build the very best experience for its customers. Ki has big ambitions but needs more excellent minds to challenge the status-quo and help it reach new horizons. Where you come in? We're looking for an MLOps Engineer to join our Algorithmic Engineering team. In this role, you’ll help us build and scale our MLOps system, enabling our underwriting algorithm to grow and scale. You’ll collaborate with colleagues across the business to solve complex technical challenges, such as generalising our MLOps system to manage actuarial and rules-based models. You’ll have the autonomy to propose, design, and execute innovative initiatives, delivering high-impact features as part of our forward-thinking team. As a commercially focused, multi-disciplined team, we combine deep expertise in specialty insurance with scalable algorithm product development to power our digital underwriting. Together, we’ll invest in iterative development and research to continuously improve the Ki platform. What you will be doing: 🖋️ Collaborate with colleagues to design, deliver, and evolve Ki's end-to-end MLOps system. Empower colleagues across Ki to deliver models into production more quickly and safely. Identify opportunities to proactively improve and extend Ki's MLOps system. Advocate and uphold model management best practices. Act as a knowledge hub on Ki's MLOps system, educating teams on its capabilities and promoting business-wide adoption. Support and mentor early-career members of the team. Champion improvements to enhance our digital underwriting capabilities. Requirements Technical Knowledge: Detailed knowledge of MLOps system development, including MLOps concepts such as feature stores, model registries, and model monitoring. Infrastructure & Tools: Understanding of infrastructure as code using tools such as Terraform. Data & Algorithms: Intermediate understanding of the control, management, and lifecycle of data products and machine learning algorithms. Industry Knowledge & Compliance: Understanding of the importance of market compliance and core regulatory requirements within the insurance space. Communication Skills: Highly effective communication and collaboration skills to educate colleagues, act as an internal knowledge hub, and promote business-wide adoption of MLOps capabilities. Leadership & Mentorship: Ability to mentor, support, and help develop early-career team members while collaborating effectively in multi-disciplined teams. Commercial Acumen: A commercially focused approach to aligning technical MLOps initiatives with specialty insurance objectives and scalable algorithm product development. Additional Expertise (Desirable): Knowledge of inference graphs, model workflows, or leveraging MLOps systems to productionise non-machine learning models (e.g., rules-based models) is highly advantageous. Benefits You’ll get a highly competitive remuneration and benefits package. This is kept under constant review to make sure it stays relevant. We understand the power of saying thank you and take time to acknowledge and reward extraordinary effort by teams or individuals. What to expect during the recruitment process: Initial recruiter screening call Interview with hiring manager Technical Interview (this may vary depending on the role) Values Interview