datatonic - Machine Learning Engineer

• 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.