Machine Learning Engineer

Overview

As a Machine Learning Engineer at Datatonic, you will craft production-grade Python solutions and advocate for high quality engineering. You will lead client-facing engagements, scope projects, and translate vague requirements into impactful ML models. You will apply ML fundamentals, deployment practices, and GenAI techniques to solve real business problems on Google Cloud. This role blends hands-on coding with opportunities to steer projects and influence outcomes in a consulting context.

Pay / Benefits

• 25 days holiday plus bank holidays
• Private health insurance
• Gym membership discounts
• WFH allowance
• Udemy access
• Auto-enrolment pension with employer contributions

Responsibilities

• Translate vague requirements into ML models addressing real-world problems
• Conduct ML experiments and develop models using ML libraries
• Leverage Generative AI to create innovative solutions
• Optimize ML solutions for performance and scalability
• Implement tailored ML code to meet specific needs
• Ensure efficient data flow between databases and backend systems
• Automate ML workflows with a focus on testing and reproducibility
• Design ML architectures using Google Cloud tools
• Develop production-grade software for ML and data-driven solutions

Key requirements

• 1–3 years of ML Engineer experience, preferably with consulting background
• Proficiency in Python and delivering production-ready, CI/CD-backed code
• Familiarity with Google Cloud, AWS, or Azure
• Hands-on software engineering practices
• Strong SQL knowledge for querying and data management
• Experience scaling computations with GPUs or distributed systems
• Familiarity with exposing ML components via web services (e.g., Flask)
• Strong communication and presentation skills

• clear communication
• client-facing presentation
• collaboration
• Python and ML libraries
• Google Cloud tools and services
• ML deployment and MLOps practices

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