Machine Learning Engineer

Machine Learning Engineer - Job Specification

A leading player in the cyber insurance sector is on the hunt for a Machine Learning Engineer to bolster their team. This organization prides itself on crafting a highly functional, sustainable, and scalable cyber insurance ecosystem alongside their esteemed clients and partners.

Salary: £50k-£65k dependant on experience

Key Responsibilities:

• Develop and Deploy Models: Take the lead in creating, training, and deploying cutting-edge machine learning models.
• Optimize Pipelines: Enhance the efficiency and effectiveness of data enrichment pipelines through continuous evaluation and refinement.
• Data Management: Establish robust data cleaning and ingestion processes for preparing datasets for machine learning applications.
• Collaborative Problem-Solving: Engage with data scientists to troubleshoot and solve complex issues, ensuring seamless operations.
• Stay Ahead of the Curve: Stay updated with the latest AI/ML advancements and integrate these into our processes to maintain a competitive edge.

Skills and Qualifications:

• Educational Background: Bachelor's degree in computer science, mathematics, or a related field.
• Professional Experience: At least three years in a similar role with a track record of developing and deploying machine learning models.
• Technical Proficiency: Expertise in Python and Pandas, with experience in adapting CPU-based models for GPU efficiency.
• Analytical Skills: Strong analytical abilities, especially in resolving data quality issues and enhancing model performance.
• Creative Solutions: A history of innovative problem-solving and independent solution development.
• Big Data Technologies: Knowledge of Spark or PySpark for handling large-scale data processing.
• ML Expertise: A thorough understanding of machine learning techniques and best practices in model development.

Desirable Qualifications:

• Production Experience: Experience in deploying and maintaining machine learning models in production settings.
• Advanced Techniques: Familiarity with gradient boosting and massive text embedding models.
• Software Engineering Knowledge: Proficiency in modern software engineering practices, including coding, testing, and deployment.
• Tool Proficiency: Experience with Databricks, Git, CI/CD pipelines, and software testing methodologies.

The ideal candidate will possess a blend of technical prowess and a forward-thinking approach to contribute to the company's mission of revolutionizing the cyber insurance landscape. If you are eager to tackle challenges at the intersection of AI and cybersecurity, we invite you to apply for this pivotal role and I look forward to receiving your profile.