Job Summary
The Data Scientist / AI/ML Engineer will develop and improve AI/ML solutions, including generative AI applications, to support corporate services and business functions such as risk management and security. The role involves applying technical expertise to build scalable, practical AI/ML solutions that improve operational efficiency and reduce risk. The engineer will collaborate with product leaders, business stakeholders, technical teams, and external vendors to translate business requirements into responsible, production-ready AI solutions.
Key Responsibilities
• Build, test, and improve AI/ML solutions, including generative AI use cases, for practical business applications.
• Partner with product leaders, business stakeholders, and technical teams to translate requirements into implementable AI features and model enhancements.
• Support the development and evaluation of AI models, reviewing models in progress for quality, performance, and suitability for intended purposes.
• Contribute to model quality processes, including automated evaluation pipelines and manual review workflows.
• Implement and optimize model training and inference workflows to support scalable deployment.
• Communicate technical concepts clearly to technical and non-technical stakeholders.
• Contribute to knowledge sharing and collaborate with cross-functional teams to align technical implementation with business objectives.
• Apply responsible AI practices when developing and evaluating AI/ML solutions.
Required Qualifications
• Master's or Ph.D. in Computer Science, Machine Learning, Operations Research, or a related field.
• 3+ years of industry experience implementing natural language processing (NLP) or generative AI solutions.
• Experience building, scaling, and optimizing training and inference APIs for deep neural networks.
• 7+ years of coding experience with languages and frameworks such as Python and PySpark.
• Strong understanding of the generative AI landscape, including recent advancements, emerging trends, and responsible AI practices.
• Strong communication skills, with the ability to explain complex technical concepts to technical and non-technical audiences.
• Experience working in cross-functional teams and balancing technical implementation with business objectives.
