About the role
gnani.ai is hiring AI/ML Interns (+PPO) for its Bengaluru office. Interns will work on real-world machine learning and Generative AI projects involving LLMs, NLP, Computer Vision, RAG, and MLOps. This internship offers a Pre-Placement Offer (PPO) opportunity for high-performing candidates. How to Apply: Email your resume to **nalina.k@gnani.ai**.
Responsibilities
- Build and deploy AI/ML solutions
- Work on LLM and Generative AI projects
- Develop NLP and Computer Vision applications
- Build Retrieval-Augmented Generation (RAG) systems
- Work on predictive analytics and recommendation systems
- Collaborate on model deployment and MLOps workflows
Requirements
- 2026, 2027, or 2028 batch
- Currently pursuing a Bachelor's or Master's degree from a Tier-1 institute (IITs, NITs, IIITs, BITS, IISc, VIT, or equivalent)
- Hands-on Machine Learning projects
- Strong Python programming skills
- Knowledge of PyTorch or TensorFlow
- Knowledge of Scikit-learn, SQL, Git, and GitHub
- Experience with LLMs, NLP, Computer Vision, RAG, or MLOps is preferred
Benefits
- ₹40,000 - ₹50,000/month stipend
- Pre-Placement Offer (PPO) opportunity
- Work on cutting-edge AI and Generative AI projects
- Mentorship from experienced AI engineers
- Exposure to production-grade AI systems
Required Skills
Frequently Asked Questions
What is the salary for AI/ML Intern (+PPO)?
The listed salary for AI/ML Intern (+PPO) at gnani.ai is ₹40,000 - ₹50,000/month.
Where is this AI/ML Intern (+PPO) role located?
This position is based in Bengaluru, India (Onsite).
What experience is required for AI/ML Intern (+PPO)?
Candidates are expected to have Freshers of experience for this role.
How do I apply for AI/ML Intern (+PPO) at gnani.ai?
Apply directly through the application link on this HireDoor job page for AI/ML Intern (+PPO) at gnani.ai.
What are the key benefits for AI/ML Intern (+PPO)?
Key benefits include: ₹40,000 - ₹50,000/month stipend; Pre-Placement Offer (PPO) opportunity; Work on cutting-edge AI and Generative AI projects; Mentorship from experienced AI engineers; Exposure to production-grade AI systems.