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

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

Solid 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

📌 AI Research Scientist (Bengaluru)
🏢 RDAlabs
📍 Bengaluru