DE&A - AIML - Data Science - Conventional AI - Search

- Strong foundation in mathematics: linear algebra, probability, stochastics, optimization theory

- Expertise in mathematical programming, algorithm design, and optimization techniques

- Skilled in formulating complex problems and designing scalable algorithms (e.g. linear/non-linear programming, convex optimization, combinatorial algorithms, etc.) and experience improving algorithms for efficiency and scalability

- Deep knowledge of machine learning, deep learning, and statistical modeling

- Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)

- Hands-on experience with neural networks, transformers, diffusion models, or generative modeling

- Familiarity with NLP, computer vision, or domain-specific AI applications

- Proficient in model evaluation, validation, and performance metrics

- Experience with AI/ML frameworks and libraries (e.g. TensorFlow, PyTorch)

- Familiarity with software development practices (version control, testing, GPU accelerated computing)

- Strong analytical thinking and problem-solving skills

- Ability to derive insights and prove algorithmic effectiveness through rigorous logic and math

- Demonstrated creativity in tackling open-ended research and real-world AI challenges

- Clear communicator, able to translate complex ideas for technical and non-technical audiences

- Effective collaborator in cross-functional teams with researchers, engineers, and business partners

- Skilled in writing technical documentation, reports, and academic publications is a plus

- Passion for AI advancement and continuous learning

- Active interest in emerging AI research, with contributions to publications, open-source projects or conference preferred

- Ph.D.’s or Master’s in Computer Science, Applied Mathematics, Engineering, or related field with AI/Optimization focus

- Proven experience in applied AI Research with deployment of AI solutions in real-world settings