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Remote MLOps Engineer (Insurance, EU/UK, 6 months contract)
Job summary
A leading technology company is seeking an experienced MLOps professional to join their remote team. This role emphasizes flexibility, allowing you to work from anywhere with varied hours. You will collaborate with international clients and contribute to impactful machine learning workflows. The ideal candidate should have hands-on experience with feature stores and MLflow, along with a strong background in data pipeline development. This position offers opportunities for bonuses and a supportive team environment.
Qualifications
- Practical experience working with feature stores in a production environment.
- Solid understanding and hands-on experience using MLflow for experiment tracking and model versioning.
- Strong skills in designing robust and scalable data pipelines.
- Experience deploying and maintaining machine learning systems in production.
- Experience with Google Cloud Platform or similar cloud environments.
- Experience in machine learning or data engineering.
Responsibilities
- Enjoy the freedom to work from anywhere with flexible hours.
- Collaborate directly with international teams.
- Earn bonuses for referrals and new business.
- Join a supportive team with no micromanagement.
Skills
Our broker platform is the core technology crucial to success – allowing us to evolve underwriting intelligently and unlock massive scale. We’re a multi-disciplined team, bringing together expertise in software and data engineering, full stack development, platform operations, algorithm research, and data science. Our squads focus on delivering high-impact features – we favour a highly iterative, analytical approach.
We’re looking for an MLOps to join our team.
- Location: UK or Europe (100% remote)
- Start Date: ASAP
- Length: 6 months contract
- Experience with Feature Stores: Practical experience working with feature stores in a production environment.
- MLflow Expertise: Solid understanding and hands-on experience using MLflow for experiment tracking, model versioning, and deployment workflows.
- Data Pipeline Development: Strong skills in designing, building, and maintaining robust, scalable, and efficient data pipelines.
- Infrastructure Proficiency: Solid infrastructure experience, including deploying, monitoring, and maintaining machine learning systems in production.
- Cloud Experience (Preferred): Experience working with Google Cloud Platform (GCP) or similar cloud environments (AWS, Azure).
- General ML/Engineering Background: Reasonable overall experience in machine learning or data engineering, with a proven ability to contribute to end-to-end ML workflows.
SUMMARY
- Work your way– Enjoy the freedom to work from anywhere, with flexible hours that match your natural rhythm.
- Work with global clients– Collaborate directly with international teams to create real impact.
- Make extra cash– Earn bonuses for referring great people or bringing in new business opportunities.
- Great people, no micromanagement– Join a supportive, results-focused team where you’re trusted to do your best work.
This flexibility allows developers…
- A better work-life balance
- Increased productivity
- The ability to work any time around the clock
- Reduction in commute time
- Design your ideal daily schedule.
- Build a career, not just a job.
- Work smarter, not longer.
- More time with family and friends
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.