Al Engineer

at  Additiv AG

YOUR QUALIFICATIONS, SKILLS AND EXPERIENCE

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field
• Proven experience in developing and deploying Al solutions, with a strong understanding of machine learning algorithms and techniques
• Proficiency in programming languages such as Python, C#
• Experience with Al frameworks and libraries, such as TensorFlow, PyTorch, and scikit learn
• Familiarity with cloud platforms, particularly Microsoft Azure, for Al model deployment and management
• Strong problem-solving skills and the ability to translate business requirements into technical solutions

ABOUT US

additiv enables leading financial institutions and brands globally to develop new and transform existing business models, capitalizing on digital innovation and Finance-as-a-Service capabilities.
additiv’s API-first cloud platform is one of the world’s most powerful solutions in wealth management, credit, and insurance. The technology, together with the global ecosystem of regulated financial service providers, opens new opportunities for banks, insurance companies, asset managers, IFAs but also ‘consumer brands’ to offer their own and third-party financial solutions quickly and flexibly via existing or new customer channels.
Headquartered in Switzerland, with regional offices in Singapore, UAE, and Germany and 300 employees, additiv serves over 400 financial companies (banks, insurers, asset managers, pension providers, IFAs etc) and brands globally.
For more information, please visit www.additiv.com

ABOUT THIS ROLE

As an Al Engineer, you will be responsible for designing, developing, and implementing Al solutions that address complex business challenges. Leveraging your expertise in artificial intelligence, machine learning, and software engineering, you will collaborate with cross functional teams to deliver impactful solutions that drive value for our organization and our clients.

YOUR RESPONSIBILITIES

• Solution Development: Design, develop, and deploy Al models and algorithms to solve business problems, leveraging a combination of supervised and unsupervised learning techniques
• Al Project Leadership: Lead Al projects from conception to delivery, working closely with stakeholders to understand requirements, define project scope, and ensure successful implementation
• Technology Utilization: Utilize cutting-edge Al technologies and tools, such as TensorFlow, PyTorch, and Microsoft Azure, to develop and deploy Al solutions at scale
• Model Training and Optimization: Train and optimize Al models to achieve high performance and accuracy, utilizing techniques such as hyperparameter tuning and transfer learning
• Collaboration and Stakeholder Engagement: Collaborate closely with cross functional teams, including data scientists, software engineers, and business stakeholders, to drive Al initiatives forward and ensure alignment with business objectives
• Continuous Learning and Improvement: Stay up-to-date with the latest developments in Al and machine learning research, techniques, and tools. Identify opportunities for innovation and process improvement within the organization