Gen AI - Internship

Role: AI / GenAI Intern

Location: ChennaiDuration: 6 Months Experience: 0–years (Engineering graduates with foundational coding experience and strong curiosity in AI systems)

Role Overview

As a GenAI Application Engineer (Graduate / Entry-Level), you’ll work with senior engineers to design, build, and test real-world AI applications powered by Large Language Models (LLMs). You’ll gain hands-on exposure to Retrieval-Augmented Generation (RAG), AI orchestration frameworks, and modern cloud deployment practices, while learning how to make GenAI systems safe, scalable, and reliable.

Responsibilities:

Assist in developing and experimenting with AI agents using frameworks such as LangChain, LlamaIndex, or CrewAI, under mentorship. Learn to integrate AI models with APIs, databases, and business tools to create useful end-to-end solutions. Support RAG pipeline setup and vector database integrations (e.g., Chroma, Pinecone, Weaviate) for contextual retrieval. Explore automation platforms like Zapier, n8n, or Make to connect systems and streamline GenAI workflows. Help build small AI-assisted developer tools or internal prototypes that demonstrate practical GenAI capabilities. Participate in building reliable, observable systems — learning tracing, logging, and error-handling practices. Collaborate closely with mentors, product, and engineering teams on live projects and contribute ideas for new AI-driven use cases or automation opportunities.

Required Skills & Knowledge

Strong foundation in Python (FastAPI/Flask) and understanding of RESTful APIs and JSON-based integrations. Basic familiarity with GenAI concepts — prompts, embeddings, RAG, and model inference. Exposure to orchestration and automation frameworks (LangChain, LlamaIndex, CrewAI, Zapier, n8n, Make). Understanding of databases and data structures — ability to connect GenAI apps with SQL/NoSQL systems. Introductory cloud and container knowledge (AWS, Azure, GCP, Docker) for deploying lightweight services. Curiosity for observability and reliability practices — logging, monitoring, and basic debugging. Collaborative mindset — strong communication skills, eagerness to learn, and openness to feedback from mentors.

Preferred Education

Bachelor’s degree in Computer Science, Information Technology, Electronics, or related field. Prior internship or academic project in AI/ML, NLP, or full-stack development is advantageous.

Soft Skills

Curious, fast learner, and comfortable experimenting with new AI and automation tools. Team player who values feedback, collaboration, and structured problem solving. Clear communicator with analytical and creative thinking skills. Organized, self-driven, and eager to translate learning into working prototypes.