Data Engineer

Data Engineer / Software Engineer
Location: Bristol (Hybrid - 2 days in-office)

A leading Insurtech company in Bristol is seeking a skilled and versatile Data Engineer / Software Engineer to join their dynamic team. This hybrid role requires at least two days a week in the office, supporting the company's continued growth. They are looking for a talented, enthusiastic individual to help drive the development and enhancement of AI/ML-powered data enrichment pipelines and processes.

The ideal candidate will have strong Python skills, a creative problem-solving mindset, and a passion for working with cutting-edge AI/ML systems and models.

Key Responsibilities:
Optimize and Enhance Pipelines: Continuously evaluate, refine, and improve the performance of data enrichment pipelines to ensure they are efficient, reliable, and scalable.
Data Management: Design and implement robust data cleaning, ingestion, and preparation processes to support analytical and machine learning models.
Collaborative Problem-Solving: Work closely with data scientists to identify, troubleshoot, and resolve complex issues, ensuring seamless and efficient operations.
Model Development and Deployment: Contribute to the development, training, monitoring, and deployment of state-of-the-art machine learning models.
Innovation and Continuous Learning: Stay updated on the latest advancements in data processing, AI/ML, and apply these innovations to improve internal systems.
Flexible Engineering Approach: Collaborate across various engineering roles, such as backend technologies and API development, often outside the traditional scope of data engineering.
Skills and Qualifications:
Education: Bachelor's degree (or equivalent) in computer science, mathematics, or a related field.
Experience: Minimum of 3 years in a similar role, with proven success in developing and deploying machine learning models or data pipelines.
Technical Skills: Strong proficiency in Python, with hands-on experience in PySpark or Pandas.
Software Engineering Expertise: Knowledge of modern software engineering practices, including coding standards, testing, and deployment best practices.
Problem-Solving: Strong analytical and problem-solving abilities, especially related to data quality and model performance improvements.
Creativity and Innovation: Demonstrated ability to think creatively and independently deliver innovative solutions.
Desirable Qualifications:
Production ML Experience: Hands-on experience deploying and maintaining machine learning models in production environments.
Advanced Techniques: Familiarity with gradient boosting methods and large-scale text embedding models.
Tool Proficiency: Experience working with Databricks, Git, CI/CD pipelines, and advanced software testing approaches.
ML Expertise: Deep knowledge of machine learning techniques and best practices in model development.
GPU Optimization: Experience in converting and optimizing CPU-based models and algorithms to run efficiently on GPUs is a plus.

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