Applied AI ML - Associate/VP

Job description

We’re looking for an Applied AI Scientist with strong machine learning experience to join our AI Technologies team. In this role, you’ll help build practical machine learning and generative AI solutions that can move from experimentation into production. You’ll work closely with product owners, data engineers, software engineers, and other ML practitioners to design, test, improve, and deploy AI/ML capabilities at scale.

This is a hands-on role for someone who enjoys solving technical problems, writing code, experimenting with new approaches, and turning ideas into reliable systems.

Job Responsibilities

• Apply machine learning, deep learning, and generative AI techniques to business problems
• Design, run, and evaluate ML experiments using current tools and frameworks
• Work with large language models, AI agents, and related methods to enhance ML workflows
• Write production-quality code and own solutions from proof of concept to deployment
• Improve model accuracy, reliability, and performance by identifying optimization opportunities
• Partner with product and engineering teams to build scalable and maintainable solutions
• Contribute technical expertise to model design and platform implementation decisions
• Share knowledge and help elevate the technical standard of our ML work Required Qualifications, Capabilities, and Skills: STEM degree or equivalent practical experience Applied experience with machine learning and deep learning methods
• Proficiency in Python and programming experience in Java, C/C++, or similar languages
• Ability to take ownership of tasks and deliver results with limited supervision
• Strong attention to detail and follow-through Effective communication skills and ability to collaborate in a team environment Experience working with engineers, product managers, and ML practitioners Preferred Qualifications, Capabilities, and Skills: Experience with large language models, including agents, planning, or reasoning techniques Experience building and deploying ML models on cloud platforms
• Familiarity with AWS tools such as SageMaker, EKS, or similar services