RDAlabs

Experience: 3–5 years of hands-on experience developing AI/ML algorithms and solution frameworks 2-3 years of experience in one or more domains: language model, computer vision, signal processing, generative AI, optimization programming, recommendation systems, or autonomous agents 1+ year of experience in agentic AI and/or AI reasoning, Digital Twins, Decision/Prescriptive AI 3-5 years of programming in Python and C++. CUDA is a plus Skills: 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 Passion for AI advancement and continuous learning Active interest in emerging AI research, with contributions to publications, open-source projects or conference preferred Preferred Skills: 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 Education : 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