Architectural Leadership
●Own the end-to-end mathematical and statistical architecture of complex AI systems, from data integration and feature engineering to model training, inference, and monitoring.
●Design scalable, robust and secure AI models and algorithms capable of handling large-scale, complex enterprise systems, grounded in first-principles mathematical design.
●Establish architectural standards, design principles, and best practices for AI-centric systems across the organization.
Applied Mathematics & Statistical Modeling
●Translate business and domain problems into mathematical, statistical, and agentic models.
●Apply foundational techniques from linear algebra, optimization, numerical methods, statistics, causal inference to improve model robustness and performance.
●Guide teams in selecting appropriate modeling approaches, loss functions, evaluation metrics, and validation strategies.
Deep Learning
●Design, develop, and deploy advanced deep learning models (e.g., transformers, state-space, diffusion models)
●Oversee the full model lifecycle, including experimentation, training, evaluation, deployment, and continuous improvement.
●Ensure models are production-ready, explainable where required, and aligned with business objectives.
Model Optimization & Performance Engineering
●Optimize models and pipelines for speed, scalability, memory/compute efficiency, and accuracy.
●Apply techniques such as pruning, quantization, distributed training, and GPU/accelerator optimization.
●Collaborate with platform and infrastructure teams to maximize system-level performance.
Technical Mentorship & Influence
●Act as a technical mentor and thought leader for AI engineers, AI scientists and software developers.
●Review designs, code, and models, providing guidance on architecture, quality, and maintainability.
●Elevate modeling maturity by promoting best practices in model design, algorithm development, coding, testing, and documentation.
Strategy, Research & Innovation
●Evaluate, select, and evolve AI frameworks, libraries, tools, and cloud platforms.
●Stay current with cutting-edge research, industry trends, and emerging technologies in AI and applied mathematics.
●Drive innovation by identifying opportunities to apply advanced AI techniques to business challenges.