DEEP LEARNING INTERN

About the program EduRankAI invites applications for the position of Deep Learning Intern to join its Artificial Intelligence and Deep Learning Research team. This fulltime internship is designed for students and recent graduates who are passionate about Deep Learning, Artificial Intelligence, Neural Networks, Computer Vision, Natural Language Processing NLP, Multimodal AI, Large Language Models LLMs, and cuttingedge machine learning research. As a Deep Learning Intern, you will contribute to the development, training, optimization, and evaluation of advanced neural network architectures that power intelligent tutoring systems, assessment platforms, research tools, computer vision applications, language models, and multimodal AI solutions across the EduRankAI ecosystem. As a Deep Learning Intern, you will collaborate with AI researchers, machine learning engineers, software engineers, and product teams to design, implement, train, and optimize deep learning models for realworld applications. Your responsibilities will include developing and training convolutional neural networks CNNs, Transformerbased architectures, and multimodal models preparing and preprocessing datasets implementing data augmentation techniques conducting hyperparameter tuning and architecture optimization profiling GPU utilization and improving training efficiency evaluating model performance using standard benchmarking methodologies analysing experimental results implementing transfer learning and finetuning techniques maintaining reproducible machine learning experiments documenting model architectures, experiments, and findings contributing to internal AI research initiatives and collaborating with multidisciplinary teams to improve productiongrade AI systems. Applicants should possess a strong understanding of Python programming, deep learning fundamentals, machine learning, linear algebra, calculus, probability, statistics, optimization techniques, and software engineering principles. Familiarity with PyTorch, TensorFlow, Keras, CUDA basics, Hugging Face Transformers, OpenCV, NumPy, Pandas, Scikitlearn, Jupyter Notebooks, Git, Linux, experiment tracking tools such as MLflow or Weights Biases WB, GPU computing, cloudbased AI development environments, or distributed training frameworks will be considered an advantage but is not mandatory. Knowledge of Computer Vision, Natural Language Processing NLP, Generative AI, Large Language Models LLMs, diffusion models, reinforcement learning, multimodal learning, transfer learning, or model deployment will also be beneficial. Candidates with academic projects, AI competitions, hackathons, research publications, opensource contributions, or personal deep learning projects are strongly encouraged to apply. Throughout the internship, participants will receive structured mentorship, continuous technical guidance, research discussions, code reviews, and exposure to modern deep learning workflows used in production and research environments. Interns will gain practical experience in neural network development, GPUaccelerated training, model optimization, experiment tracking, AI evaluation, technical documentation, collaborative engineering, and research methodologies while contributing directly to live Artificial Intelligence and Deep Learning initiatives. We are looking for individuals with exceptional analytical ability, mathematical aptitude, logical reasoning, scientific curiosity, creativity, problemsolving skills, attention to detail, communication skills, ownership, adaptability, teamwork, engineering discipline, research orientation, and a continuous learning mindset. The internship is primarily conducted onsite at the EduRankAI campus with structured mentorship and collaborative engineering activities. Remote participation may be permitted only in exceptional, preapproved cases. Applications submitted under EduRankAI's Talent Accessibility Initiative are completely free. This is an unpaid internship intended to provide meaningful industry exposure, structured mentorship, handson Deep Learning experience, professional portfolio development, and opportunities for outstanding performers to be considered for advanced AI research projects, internship extensions, preplacement interviews, research collaborations, or future fulltime opportunities based on performance and organizational requirements. Perks Certificate of Completion Letter of Recommendation for exceptional performers Mentorship from Senior AI Researchers and Deep Learning Engineers Hands-on experience with production-grade Deep Learning and AI systems Exposure to GPU computing, neural network optimization, and multimodal AI Opportunity to contribute to live Artificial Intelligence research and product development Professional Deep Learning portfolio development Cross-functional collaboration with AI research, engineering, product, and platform teams Performance-based Pre-Placement Interview opportunity Exposure to state-of-the-art Deep Learning, Large Language Models, Computer Vision, and Generative AI technologies Who can apply? Only those candidates can apply who: are from Any and specialisation from Any are available for duration of 3 Months have relevant skills and interests Terms of Engagement This is a full-time internship requiring a commitment of six days per week, for a duration of three months. The internship is primarily conducted on-site at the EduRankAI campus with structured mentorship and collaborative engineering activities. Remote participation may be permitted only in exceptional cases with prior organizational approval. Interns will contribute to live Deep Learning and Artificial Intelligence projects while maintaining professionalism, confidentiality, documentation standards, engineering best practices, research integrity, and project timelines. Participants will collaborate with multidisciplinary teams, develop and optimize neural network architectures, conduct AI experiments, evaluate model performance, prepare technical documentation, participate in research discussions, and support continuous improvements to production AI systems. Successful completion of the internship will be evaluated based on technical competency, model quality, analytical ability, research contribution, documentation standards, collaboration, innovation, ownership, consistency, professionalism, and overall contribution throughout the internship. Number of openings 10