Machine Learning Engineer

Role Purpose:

• Support the development and maintenance of software tools that underpin Data Science solutions and operations, using modern technologies (e.g. AWS, Docker, Kubernetes) to contribute to improved business performance.
• Collaborate with business stakeholders and engineering teams to gather requirements and contribute to the design and implementation of solutions.
• Assist in maintaining and enhancing existing tools/services developed by the Data Science team.
• Build CI/CD pipelines and contribute to testing, logging, and monitoring processes under guidance.
• Participate in code reviews and follow CarTrawler best practices for clean, maintainable code.
• Continuously develop technical skills and support the wider team, working under the mentorship of senior engineers.

Reporting to: Head of MLOPS

• Collaborating with team members and stakeholders to understand project requirements and identify areas where support is needed.
• Staying up to date with developments in Data Science and DevOps to help ensure our tech stack remains current.
• Contributing to the development of Data Science engineering solutions by:
• Supporting data collection, cleaning, and preparation based on identified requirements.
• Assisting in the implementation of engineering solutions using appropriate tools and technologies.
• Following best practices for CI/CD, testing, logging, and monitoring under guidance.
• Writing scalable, production-ready code where appropriate, with support from senior colleagues.
• Participating in deployments and monitoring the solution impact.
• Supporting ongoing development and maintenance of internal Data Science tools/platforms, such as:
• MVT – Multi-variate testing platform.
• ACDC – Cloud-based ML deployment tool.
• Action Factory – Automated decision-making platform.
• Echo – MLOps pipeline management tool.
• Various internal Python libraries and utilities.
• Assisting with BAU tasks and ongoing support of tools and services provided by the Data Science team.
• Participating in proof-of-concept projects, helping to explore innovative solutions and learning how to analyse and interpret data effectively.
• Taking ownership of personal development by seeking guidance and feedback from mentors and contributing positively to a collaborative team environment.

Experience Required:

• Undergraduate degree in Computer Science, Engineering, Mathematics, or related technical field, or relevant internship/industry experience.
• Good understanding of software engineering and DevOps practices (e.g. Jenkins, Github Actions), including Object-Oriented Programming, data structures, version control, REST APIs, and containerisation tools (e.g. Docker, Kubernetes).
• Exposure to cloud platforms such as AWS or Azure.
• Experience with developing GenAI applications, or surrounding tech stack (e,g. Vector DB, NLP, LLM) seen as a plus.
• Proficient in writing clean, structured Python code; familiarity with common Python libraries used in data science and machine learning.
• Comfortable using version control systems (e.g. Git) and basic development workflows (commits, branches, pull requests).
• Exposure to ML libraries (e.g. scikit-learn; bonus for familiarity with TensorFlow or PyTorch).
• Willingness to learn test-driven development and build basic unit and integration tests with support.
• An interest in learning about production considerations like resource constraints and scalability.
• Basic SQL skills; exposure to other database types (graph, NoSQL) is a plus.
• Experience with developing GenAI applications or of supporting tech stack seen as a plus