Datatonic

Full-stack Engineer

• Interpret vague requirements and develop models to solve real-world problems.
• Conduct ML experiments using programming languages with machine learning libraries.
• Leverage generative AI to develop innovative solutions.
• Optimise machine learning solutions for performance and scalability.
• Implement tailored machine learning code to meet specific needs.
• Ensure efficient data flow between databases and backend systems.
• Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.
• Create machine learning architectures using Google Cloud tools and services.
• Build and deploy production-grade software for ai and data-driven solutions.

Senior Engineer

Cloud Engineer

• Shape the architecture for key projects.
• Provide pre & post sales architectural advice and thought leadership for machine learning, analytics, and data migration projects.
• Help build out the architectural side of next-generation AI products.
• Utilise cutting-edge, scalable cloud and data processing technologies to develop advanced solutions for customers.
• Oversee the design and implementation of enterprise-scale Data and AI projects.
• Introduce innovative ideas and approaches to enhance capabilities and methodologies.
• Shape the architecture team and mentor team members while promoting technical excellence.
• Collaborate with sales teams to provide technical expertise during client engagements and help scope solutions for challenging requirements.
• Partner with GTM teams to create and refine new market-ready solutions with a focus on technical leadership and governance.
• Conduct architecture reviews for key clients, delivering actionable recommendations to senior stakeholders.
• Define and embed best practices into project delivery, ensuring quality and consistency across initiatives.
• Contribute to the internal knowledge base, including technical collateral and thought leadership materials.
• Work in a dynamic, agile environment alongside data scientists, machine learning experts, data analysts, and data engineers.
• Collaborate closely with partners such as Google Cloud, Tableau, and Looker to leverage their technologies effectively.
• Guide team members, fostering a culture of growth and innovation.

Senior Cloud Architect

• 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.

• Shape the architecture for key projects.
• Provide pre & post sales architectural advice and thought leadership for machine learning, analytics, and data migration projects.
• Help build out the architectural side of next-generation machine learning products.
• Oversee the design and implementation of enterprise-scale Data and AI projects.
• Introduce innovative ideas and approaches to enhance Datatonic’s capabilities and methodologies.
• Shape the architecture team and mentor team members while promoting technical excellence.
• Collaborate with sales teams to provide technical expertise during client engagements and help scope solutions for challenging requirements.
• Conduct architecture reviews for key clients, delivering actionable recommendations to senior stakeholders.
• Define and embed best practices into project delivery, ensuring quality and consistency across initiatives.
• Work in a dynamic, agile environment alongside data scientists, machine learning experts, data analysts, and data engineers.

Machine Learning Engineer

• Interpret vague requirements and develop models to solve real-world problems.
• Conduct ML experiments using programming languages with machine learning libraries.
• Leverage generative AI to develop innovative solutions.
• Optimise machine learning solutions for performance and scalability.
• Implement tailored machine learning code to meet specific needs.
• Ensure efficient data flow between databases and backend systems.
• Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.
• Create machine learning architectures using Google Cloud tools and services.
• Build and deploy production-grade software for machine learning and data-driven solutions.