Intern

About The Role

We are looking for a passionate AI/ML Intern to join our team and contribute to building next-generation AI applications. You will work on machine learning models, Generative AI systems, RAG (Retrieval-Augmented Generation) pipelines, and Agentic AI frameworks, leveraging cloud platforms such as Azure AI (or similar) to develop scalable, production-ready solutions. This role is ideal for someone who has hands-on experience in Python, AI/ML frameworks, and modern GenAI techniques and is excited about applying them to solve real-world business problems.

Key Responsibilities

• Design, build, and deploy AI/ML models for classification, prediction, and optimization tasks.
• Develop and implement GenAI solutions (LLMs, prompt engineering, fine-tuning, embeddings).
• Build RAG pipelines integrating vector databases, knowledge graphs, and LLMs.
• Implement Agentic AI workflows for automation, reasoning, and multi-step task execution.
• Work with Azure AI services (OpenAI, Cognitive Search, ML Studio, Data Factory) or similar platforms (AWS Sagemaker, GCP Vertex AI).
• Optimize ML pipelines for scalability, performance, and cost efficiency in cloud and edge environments.
• Collaborate with cross-functional teams (data engineers, domain experts, product managers).
• Stay updated with the latest AI/ML/GenAI research and tools and integrate best practices.

Requirements & Tech Stack

• Programming: Strong in Python (NumPy, Pandas, PyTorch/TensorFlow, LangChain, Transformers).
• ML/AI: Model training, fine-tuning, evaluation, deployment.
• GenAI: Experience with LLMs (OpenAI, LLaMA, Mistral, etc.), embeddings, and prompt design.
• RAG Systems: Knowledge of vector databases (Pinecone, FAISS, Weaviate, Milvus).
• Agentic AI: Familiarity with AI agents (LangChain Agents, AutoGen, CrewAI, Semantic Kernel).
• Cloud: Hands-on with Azure AI (preferred) or AWS/GCP AI services.
• MLOps: Model versioning, CI/CD, deployment using MLflow/Docker/Kubernetes.
• Strong problem-solving, analytical, and debugging skills.

Qualifications

• Education: B.E./B.Tech/M.Tech in Computer Science, AI/ML, Data Science, or related field.
• Experience: 0–2 years in AI/ML development.

Good to Have

• Knowledge of NLP, Computer Vision, or Multi-modal AI.
• Experience with knowledge graphs or graph databases.
• Exposure to edge AI deployments.
• Contributions to open-source AI/ML projects.