AI Engineer Intern Internship in Bangalore (Hybrid)

Selected Intern's Day-to-day Responsibilities Include

• Work closely with the founding team on real AI model development projects
• Assist in fine-tuning LLMs under guidance from senior engineers
• Support model evaluation and benchmarking efforts across different architectures
• Learn advanced optimization techniques like LoRA, QLoRA, and parameter-efficient methods
• Gain exposure to RLHF workflows and human preference learning
• Participate in training pipeline development and understand the full model lifecycle
• Contribute to research experiments and help implement cutting-edge techniques
• Support the creation of new AI solutions that solve real-world problems
• Present findings and participate in technical discussions with the team
  

• Currently pursuing Bachelor's or Master's degree from IIT, NIT, BITS, or equivalent in Computer Science, AI/ML, or related field
• Strong computer science fundamentals including algorithms, data structures, and mathematics
• Solid foundation in linear algebra, statistics, and optimization
• Final year undergrad or recent graduate preferred
• Strong Python programming skills with experience in machine learning libraries
• Academic or project experience with deep learning frameworks (PyTorch, TensorFlow)
• Understanding of neural network architectures and training processes
• Familiarity with Transformers and attention mechanisms (through coursework or projects)
• Basic knowledge of LLMs and their applications
• Experience with Jupyter notebooks and experimental workflows
• Personal projects involving fine-tuning or working with pre-trained models
• Research experience or publications in AI/ML (even if academic)
• Participation in ML competitions (Kaggle, hackathons, etc.)
• Open-source contributions to AI/ML projects
• Any exposure to LoRA, QLoRA, or RLHF concepts (even theoretical)
• Internship experience at AI/ML companies or research labs
  

What You Will Gain

• Direct mentorship from founding team and senior AI engineers
• Weekly research discussions and paper reading sessions
• Hands-on experience with state-of-the-art models and techniques
• Access to high-end GPUs and computational resources
• Opportunity to co-author research papers or technical blogs
• Production-level experience with LLM fine-tuning and optimization
• Deep understanding of modern AI architectures and training techniques
• Hands-on experience with RLHF and advanced model alignment methods
• Research skills in evaluating and benchmarking AI models