Datatonic
AI & Data
SeniorEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Engineer at Datatonic. Skills: Python, Machine Learning, Google Cloud. Develop models. Conduct ML experiments”
What You'll Achieve.
future-proof operations; unlock actionable insights; stay ahead of curve
Industry & Context.
solve problems
What They're Looking For.
Must Have
Python, Google Cloud, AWS, Azure, SQL, GPUs, distributed computing systems, web services, Flask
Nice to Have
consulting background, Scale-up experience, Google Cloud Professional Machine Learning Engineer, AWS Solution Architect
What You'll Do.
Conduct ML experiments
Leverage generative AI
Optimise ML solutions
Implement custom code
Ensure efficient data flow
Automate ML workflows
Create ML architectures
Build production-grade software
How You'll Work.
Team & Collaboration
Lead client discussions; Build trusted relationships
Communication Scope
communication; presentation
Process & Methodology
Scoping projects, Overseeing delivery
Full Job Description
Senior Machine Learning Engineer SHAPE THE FUTURE OF AI & DATA WITH US At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud Platform. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world. YOUR MISSION As a Senior Machine Learning Engineer, you'll know how to engineer beautiful code in Python and take pride in what you produce. You'll be an advocate of high-quality engineering and best-practice in production software as well as rapid prototypes. Whilst the position is a hands-on technical role, we'd be particularly interested to find candidates with a desire to lead projects and take an active role in leading client discussions. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements. 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. WHAT YOU’LL DO - 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
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