Senior Machine Learning Engineer

at  Policy Expert

ABOUT US

Our ambition is to be the most successful insurance disruptor that customers want to stay with for life. With double-digit growth and a commitment to customer-centric solutions, we are challenging the norms of an industry known for low trust and high switching rates. Our goal is to build deeper relationships with our customers, transforming insurance from a costly necessity to a valued support system.
In 2023, we were honoured with an Outstanding 2-star accreditation and ranked among the Top 100 Best Large Companies to Work for by Best Companies.

YOUR DAY-TO-DAY

We are seeking a Senior Machine Learning Engineer to join our ML engineering team. The ideal candidate will have strong experience in designing, implementing, and optimising machine learning models and systems. You will play a pivotal role in architecting scalable ML solutions and collaborating with cross-functional teams to integrate these solutions into our product offerings.

• Develop and maintain scalable machine learning models and pipelines, with a focus on deployment via AWS Sagemaker.
• Implement MLOps frameworks to streamline model lifecycle around MLFlow, ensuring robust training, validation, deployment, and monitoring.
• Collaborate with data scientists and product teams to translate business needs into effective ML strategies.
• Optimise data processing workflows using tools like Spark, Docker, and cloud-native solutions.
• Mentor and educate team members on ML engineering best practices.
• Stay current with machine learning advancements, advocate for their integration, and lead the evaluation of tools to enhance workflows.

We are seeking a Senior Machine Learning Engineer to join our ML engineering team. The ideal candidate will have strong experience in designing, implementing, and optimising machine learning models and systems. You will play a pivotal role in architecting scalable ML solutions and collaborating with cross-functional teams to integrate these solutions into our product offerings.

• Develop and maintain scalable machine learning models and pipelines, with a focus on deployment via AWS Sagemaker.
• Implement MLOps frameworks to streamline model lifecycle around MLFlow, ensuring robust training, validation, deployment, and monitoring.
• Collaborate with data scientists and product teams to translate business needs into effective ML strategies.
• Optimise data processing workflows using tools like Spark, Docker, and cloud-native solutions.
• Mentor and educate team members on ML engineering best practices.
• Stay current with machine learning advancements, advocate for their integration, and lead the evaluation of tools to enhance workflows

This role will be based in our London office in a Hybrid mode.

• We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team