Design and develop robust AI architectures, frameworks, and algorithms that can support large-scale and complex enterprise SaaS AI solutions in alignment with business objectives.
Evaluate and select appropriate AI technologies, tools, and frameworks to achieve desired performance, accuracy, and scalability.
Collaborate across the company to guide the direction of machine learning at Qualtrics, spanning teams from research to production.
Communicate with a team of research scientists, product managers, and engineers and lead and document AI architectures, design decisions, and technical specifications for reference and knowledge sharing.
Work closely with research scientists and model engineering teams on developing and deploying ML systems in production. Develop a strategy for optimizing models and systems for performance, scalability, efficiency, and cost.
Offer technical supervision and direction to the ML platform teams and lead the creation of future ML platforms for deploying and monitoring AI models in production settings while maintaining compliance with the best practices in machine learning and deep learning.
Conduct regular code reviews and provide technical guidance to team members.
Stay up-to-date with the latest advancements in AI technologies, frameworks, and algorithms, and identify opportunities for their application in the organization.
Requirements & Skills:
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
Proven experience (7+ years) working as a Machine Learning Engineer or related role.
Proficiency in programming languages such as Python, Java, or C++ and popular AI libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).
Solid understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms.
Excellent problem-solving and analytical skills, with the ability to break down complex problems into actionable components.
Strong communication and collaboration skills, with the ability to work effectively within cross-functional teams.
Ability to stay updated with the latest AI technologies, frameworks, and platform advancements.
Knowledge of deploying and optimizing LLMS with open-source frameworks like DeepSpeed, Accelerate, FasterTransformer, and Transformers-NeuronX libraries is a plus.
Knowledge of ethical considerations and responsible AI practices is a plus.
