- 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
- Robust 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
- Experience in regulated industries (e.g., finance, healthcare, insurance)
- Excellent communication and stakeholder engagement skills
- Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
- Proficiency in model evaluation, distributed training, and hyperparameter optimization
- Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
- Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
- Proficient in API, MCP and Microservices technologies
- Track records in large-scale, real-time AI/GenAI/AgenticAI/ML database and solution technologies
- Background in responsible AI/ML, model interpretability, and fairness auditing
- 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

📌 DE&A - AIML - Data Science - Conventional AI - Search (Pune)
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