o9 Solutions - Senior Architect - Applied Research & Deep Learning

Job Description :We are seeking a highly experienced, intellectually curious Senior Architect with a strong foundation in Applied Mathematics, Statistical Modeling, and Deep Learning to lead the design, implementation and delivery of next-generation AI models and algorithms.In this role, you will operate at the intersection of business problem-solving, mathematical modeling, software development and Agentic AI development.You will translate complex real-world problems into formal mathematical and statistical representations, design and implement advanced deep learning and Agentic systems, and ensure their reliable deployment in production environments.The ideal candidate combines software engineering, statistical modeling, AI techniques, and a strong passion for applied mathematics and innovation, with the ability to influence technical direction across teams.What you will do :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.What you will have :Education :- Masters or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a closely related quantitative discipline.Professional Experience :- 10+ years of overall software engineering experience.- 5+ years of hands-on experience in applied mathematics, statistical modeling, and deep learning systems.- Demonstrated experience architecting and deploying AI solutions in production environments.Deep Learning Expertise :- Strong experience designing, training, and deploying deep learning models using modern AI frameworks.- Solid understanding of the full ML lifecycle, including experimentation, deployment, monitoring, and retraining.- Experience working with large datasets and distributed or high-performance computing environments.Mathematics & Statistics :- Deep expertise in linear algebra, multivariate calculus, probability theory, optimization, statistical modeling and causal inference.- Ability to reason formally about model behavior, convergence, and performance trade-offs.Programming & Engineering Skills :- Expert-level proficiency in Python, including state-of-the-art mathematical, statistical, ML and agentic libraries.- Strong software engineering fundamentals: data structures, algorithms, system design, and performance optimization.- Experience with additional languages such as Scala, Kotlin or Rust is highly desirable. (ref:hirist.tech)