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

P assion for AI advancement and continuous learning

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