As the Google Cloud AI Partner of the Year, Datatonic are a team of data experts who enable businesses to accelerate impact through cloud data engineering, advanced analytics and AI.

Datatonic

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machine-learning-engineer

Datatonic is your go-to AI partner that addresses complex business challenges through advanced cloud data and AI consultancy. They offer a range of services including generative AI applications for various industries such as Retail, Telecommunications, Gaming, and Financial Services. With a focus on data-driven decision-making and analytics, Datatonic empowers businesses to optimize their operations and enhance customer engagement across multiple sectors.

As the Google Cloud AI Partner of the Year, Datatonic are a team of data experts who enable businesses to accelerate impact through cloud data engineering, advanced analytics and AI. 

• Act as the lead technical authority in high-stakes engagements
• Partner with the commercial team to architect winning solutions
• Lead the delivery of enterprise-grade systems such as GenAI agents, real-time recommendation engines, or predictive maintenance models
• Own the complete technical lifecycle for projects, designing end-to-end ML architectures on GCP and implementing robust MLOps pipelines
• Collaborate with the Head of Delivery to define the technical DNA of ML practice, evolving best practices
• Spearhead the development of internal accelerators and reusable frameworks
• Formally mentor and coach junior and mid-level engineers through code reviews and technical guidance

Lead Machine Learning Engineer

Lead Machine Learning Engineer at Datatonic, shaping AI and Data solutions for global businesses. Oversee ML project delivery and mentor engineering talent remotely from Canada.

data-engineer

• Build the infrastructure that enables analytics and data science teams to deliver innovative, impactful solutions for clients.
• Assist clients in migrating their existing business intelligence and data warehouse solutions to Google Cloud.
• Design, develop, and optimize robust data pipelines, making data easily accessible for visualization and machine learning applications.
• Design and implement new data warehouse and data mart solutions, including transforming, testing, deploying, and documenting data.
• Understanding data modeling techniques.
• Optimising and storing data for warehouse technologies.
• Architect, maintain, and troubleshoot cloud-based infrastructure to ensure high availability and performance.
• Work closely with technology partners such as Google Cloud, Snowflake, dbt, and Looker, mastering their technologies and building a network with their engineers.
• Collaborate in an agile and dynamic environment with a team of data engineers, BI analysts, data scientists, and machine learning experts.
• Implement software engineering best practices to analytics processes, such as version control, testing, and continuous integration.

Senior Data Engineer

Data Engineer working with Google Cloud's technologies, assisting clients in data solutions and pipelines. Collaborating with teams to optimize data infrastructures and promote agile practices.

devops-engineer

• Design, implement, and automate scalable and resilient CI/CD & DevOps pipelines on Google Cloud Platform, tailored to client-specific AI/ML and data engineering needs.
• Develop and maintain IaC pipelines using tools like Terraform to automate infrastructure provisioning and deployment on GCP, improving efficiency and consistency.
• Define and embed best practices into our internal processes to enhance the quality and consistency of our work.
• Introduce innovative ideas and approaches to enhance Datatonic’s capabilities and methodologies.
• Collaborate with sales teams to provide technical expertise during client engagements and help scope solutions for requirements.
• Contribute to Datatonic’s internal knowledge base, including technical collateral and thought leadership materials.
• Work in a dynamic, agile environment alongside data scientists, machine learning experts, data analysts, architects, and data engineers.
• Collaborate closely with partners such as Google to leverage their technologies effectively.
• Guide team members, fostering a culture of growth and innovation.
• Provide expert advice to customers on DevOps best practices.
• Establish and enhance DevOps functions within clients’ organisations.
• Engage with customers and project teams throughout the project development lifecycle to ensure seamless delivery.

Senior DevOps Engineer

Senior DevOps Engineer at Datatonic, establishing and leading DevOps practices for AI & Data projects. Collaborating with sales and technical teams while mentoring for innovative growth.

• 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.

Senior Machine Learning Engineer

Senior Machine Learning Engineer developing innovative AI solutions using machine learning and data engineering principles. Collaborating with clients to deliver impactful results in a remote setting.

Datatonic

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Lead Machine Learning Engineer

Datatonic

Senior Data Engineer

Datatonic

Senior DevOps Engineer

Datatonic

Senior Machine Learning Engineer

Datatonic