Senior MLOps Engineer for Algorithmic Underwriting

Ki Insurance; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Ki Insurance in London is seeking a Senior MLOPs Engineer to join our Algorithmic Underwriting team. You will develop and extend our MLOps platform to improve underwriting algorithms and model lifecycle management.

Join a commercially focused, cross-disciplinary team delivering high-impact features with an iterative, data-driven approach, collaborating with academia and industry partners to push boundaries.

Qualifications

• Experience with MLOps system development, including feature store, model registry and model monitoring.
• Experience with infrastructure as code such as Terraform.
• Understanding of data products and machine learning algorithm governance.
• Understanding of market compliance and regulatory requirements.

Responsibilities

• Design, deliver and evolve Ki’s end-to-end MLOps system.
• Create and refine governance around model lifecycle management.
• Enable teams to deploy models quickly and safely into production.
• Manage cost and ROI related to the MLOps system, including build vs buy decisions.
• Identify opportunities to extend Ki’s MLOps system.
• Advocate and uphold model management best practices.
• Act as a knowledge hub on Ki’s MLOps system and drive adoption.
• Liaise with stakeholders to evolve the roadmap for Ki’s MLOps system.
• Coach and develop early-career team members.
• Drive improvements in digital underwriting capabilities.

Skills

MLOps

Terraform

Model monitoring

Feature store

Model registry

Regulatory compliance

Data products

Inference graphs

Rules-based models

Education

Bachelor's degree in STEM

Tools

Terraform

Job description

Ki Insurance in London is seeking a Senior MLOPs Engineer to join our Algorithmic Underwriting team. You will develop and extend our MLOps platform to improve underwriting algorithms and model lifecycle management.

Join a commercially focused, cross-disciplinary team delivering high-impact features with an iterative, data-driven approach, collaborating with academia and industry partners to push boundaries.