Robust 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)




Solid 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
Explicit 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

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