ml engineer in financial services

NewDay is a financial services company building data-driven machine-learning capabilities and production systems.

• Establish standard, reusable patterns for model serving, pipelines, and deployment;
• Define standards for model packaging, versioning, CI/CD, monitoring, and observability;
• Set production-readiness criteria for models entering engineering workflows;
• Provide technical direction for ML solutions delivered within the team;
• Evolve platform capabilities to reduce bespoke approaches over time;
• Reduce the engineering effort required to productionize models through reusable approaches and platform capabilities.

• Experience building and operating ML systems in production, including model serving, monitoring, and lifecycle management;
• Strong understanding of serving patterns across batch and real-time environments and of moving models from training to production;
• Experience defining engineering approaches that enable Data Science teams to deliver reliable, production-ready models;
• Ability to turn ambiguity into clear, adoptable technical standards and reusable patterns;
• Experience setting technical direction and influencing engineering practices across teams;
• Hands-on experience with cloud ML platforms such as AWS SageMaker and orchestration tools such as Airflow and Step Functions;
• Strong communication skills and ability to collaborate across Data Science, Engineering, and Data Platforms;
• Comfortable operating at the boundary between experimentation and production-grade ML delivery;
• Values structure, consistency, and high standards in engineering practices;
• Thinks in terms of reusable frameworks rather than one-off solutions;
• Motivated by improving the reliability and long-term maintainability of ML systems;
• Brings clarity, direction, and technical leadership to complex problem spaces.

• Equal opportunity employer with an inclusive working culture;
• Reasonable adjustments are available to support candidates with disabilities or caring responsibilities during the application and interview process.