Jobs at Datatonic

Lead Datatonic’s global Learning and Development strategy, building Google Cloud, AI, and consulting capabilities across technical and non-technical teams. Commercialize internal training products through the Datatonic Academy, support Google Cloud Authorised Training Partner activities, and drive customer education. Oversee learning pathways, certification programs, hackathons, content, incentives, apprenticeships, graduate schemes, and strategic training initiatives while influencing executives, engineers, sales teams, and enterprise clients.

Lead DevOps pipeline design and implementation, advise clients on DevOps best practices, establish DevOps functions, and engage stakeholders to ensure seamless delivery across projects.

Drive strategic revenue and transform enterprise client relationships through long-term engagements, navigating challenges and delivering measurable business value.

Lead and implement end-to-end ML solutions: translate vague requirements into models, run ML experiments, apply GenAI, design ML architectures on Google Cloud, build production-grade code, handle MLOps, data engineering, optimization, deployment, and client-facing project leadership.

As a Machine Learning Engineer, you will develop ML models, optimize solutions, automate workflows, and design ML architecture while ensuring high-quality production software delivery.

Lead pre-sales activities at Datatonic, shaping data and AI solutions, architecting transformations, and advising clients across various industries. Build trust with C-suite, manage RFPs, and represent the company in strategic engagements.

Manage Google Cloud partner portal administration, deal registration, co-sell tracking, partner onboarding, stakeholder communications, funding utilization, financial reconciliation, tier and scorecard compliance, dashboards, incentive forecasting, and joint marketing campaign reporting. The role coordinates closely with sales, marketing, finance, engineering, Datatonic leadership, Google Cloud representatives, and external partners.

Lead architecture and delivery of enterprise-scale data and AI projects on Google Cloud. Provide pre- and post-sales technical advice, design and migrate cloud solutions, mentor architects, collaborate with partners and sales, define best practices, and build next-generation ML products and scalable production-grade platforms.