datatonic - Machine Learning Engineer
Requirements
• Experience: 1-3 years as a Machine Learning Engineer, preferably with a consulting background. • Programming Skills: Proficiency in Python as a backend language, capable of delivering production-ready code in well-tested CI/CD pipelines. • Cloud Expertise: Familiarity with cloud platforms such as Google Cloud, AWS, or Azure. • Software Engineering: Hands-on experience with foundational software engineering practices. • Database Proficiency: Strong knowledge of SQL for querying and managing data. • Scalability: Experience scaling computations using GPUs or distributed computing systems. • ML Integration: Familiarity with exposing machine learning components through web services or wrappers (e.g., Flask in Python). • Soft Skills: Strong communication and presentation skills to effectively convey technical concepts. • Cloud certifications (Google CDL, AWS Solution Architect, etc.).
Responsibilities
• To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You'll be comfortable with model optimisation and deployment tools and practices. Furthermore, you'll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud. • Translating Requirements: Interpret vague requirements and develop models to solve real-world problems. • Data Science: Conduct ML experiments using programming languages with machine learning libraries. • GenAI: Leverage generative AI to develop innovative solutions. • Optimisation: Optimise machine learning solutions for performance and scalability. • Custom Code: Implement tailored machine learning code to meet specific needs. • Data Engineering: Ensure efficient data flow between databases and backend systems. • MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. • ML Architecture Design: Create machine learning architectures using Google Cloud tools and services. • Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.
Benefits
• We believe in empowering our team to thrive, with benefits including: • Holiday: 25 days plus bank holidays (obviously!) • Health Perks: Private health insurance (Vitality Health) and Smart Health Services • Fitness & Wellbeing: 50% gym membership discounts (Nuffield Health, Virgin Active, Pure Gym). • Hybrid Model: A WFH allowance to keep you comfortable. • Learning & Growth: Access to platforms like Udemy to fuel your curiosity. • Pension: (Auto-enrolment after probation period. 3% employer contributions raising 1% per year of service to a max of 10%) • Life Insurance: (3 x your base salary!) • Income Protection: (up to 75% of base salary, up to 2 years) • Cycle to Work Scheme • Are you ready to make an impact?
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