Full Stack AI Engineer Intern

AI Prof; Hyderabad; On-site; INR 279,000 - 335,000; Part time

Benefits offered by this job

Stipend ₹25k–₹30k per month

Path to full-time opportunity

Product ownership experience

Mentorship and guidance

Hands-on AI engineering exposure

Job summary

AI.Prof in Hyderabad is offering a Full-Stack AI Engineer Intern role, building AI-powered features across the stack from frontend interfaces to LLM pipelines. You will work on AI tutoring, personalized learning, and adaptive quizzes, and collaborate with a team that ships production-ready systems for learners.

You will gain hands-on experience with React/Next.js, Python/FastAPI, and RAG workflows, while implementing reliability, testing, and deployment practices.

Qualifications

• Academic standing: final-year or recent graduate with strong analytical and technical capability.
• Demonstrated track record: meaningful software or AI projects through GitHub, hackathons, internships, research, open-source contributions, or personal projects.
• Hands-on AI experience: at least one project involving LLMs, RAG, AI agents, NLP, or related tech.

Responsibilities

• Build production-ready full-stack features across React/Next.js, backend services, APIs, databases, and AI systems.
• Develop AI-native experiences including tutors, assistants, content generation, personalization, and intelligent learning workflows.
• Engineer RAG systems with document ingestion, embeddings, retrieval, and grounded generation pipelines.
• Build AI agents and agentic workflows that reason through tasks, use tools, and interact with APIs.

Skills

Full-Stack Development

React/Next.js

Backend APIs

Python/FastAPI

Git / Version Control

LangChain / LLM tools

AI/NLP fundamentals

Education

Final-year student with CGPA > 7.5

Tools

PostgreSQL

MongoDB

Docker

Git

Job description

Our Education Engineering team builds AI-native products that help students learn, practice, and make better academic and career decisions.We combine full-stack engineering, Generative AI, intelligent agents, data, and automation to turn complex AI capabilities into reliable products used by real learners.We are not building AI demos or simple chatbot wrappers. We build complete, production-oriented systems where the product experience, application layer, AI systems, data, and infrastructure work together.

About the Role

As a Full Stack AI Engineer Intern, you will build AI-powered product features across the entire stack from frontend interfaces and backend APIs to LLM pipelines, RAG systems, AI agents, databases, and deployment.You will work on problems such as AI tutoring, personalized learning, document intelligence, assessment generation, adaptive quizzes, knowledge retrieval, and intelligent student workflows.You will also continuously explore and apply modern AI engineering tools and techniques including LLM APIs, open-source models, agentic workflows, tool calling, structured outputs, multimodal AI, evaluation frameworks, and AI-assisted development to build faster and more reliable products.

What You'll Do

• Build Full-Stack Features: Design and develop production-ready features across React/Next.js, backend services, APIs, databases, and AI systems.
• Build AI-Native Experiences: Develop AI tutors, assistants, content generation, personalization, recommendations, and intelligent learning workflows using modern LLMs.
• Engineer RAG Systems: Build document ingestion, chunking, embeddings, retrieval, reranking, and grounded generation pipelines.
• Build AI Agents: Develop agentic workflows that can reason through tasks, use tools, retrieve information, interact with APIs, and execute multi-step workflows.
• Develop Backend Systems: Build scalable APIs and services using Python/FastAPI or equivalent technologies, integrating AI pipelines, databases, authentication, and external services.
• Build AI-Powered Frontends: Create intuitive interfaces for conversational AI, streaming responses, generated content, assessments, dashboards, and interactive learning experiences.
• Engineer AI Reliability: Implement structured outputs, validation, guardrails, retries, fallbacks, caching, monitoring, and error handling for production AI systems.
• Build AI Evaluation Systems: Create automated evaluations to measure model accuracy, relevance, groundedness, consistency, latency, and cost.
• Explore Multimodal AI: Experiment with models capable of understanding and generating content across text, images, documents, and other modalities.
• Leverage AI Development Tools: Use modern AI coding agents and developer tools for code generation, debugging, testing, refactoring, documentation, and codebase exploration.
• Own Features End-to-End: Take features from problem definition and architecture through implementation, testing, deployment, monitoring, and iteration.

What We're Looking For

Technical

• Full-Stack Development: Hands-on experience with React/Next.js, backend development, REST APIs, databases, Git, and modern web application architecture.
• Backend Engineering: Experience with Python frameworks such as FastAPI, Flask, or Django and databases such as PostgreSQL, MongoDB, or equivalent.
• Generative AI: Understanding of LLMs, prompting, context windows, embeddings, structured outputs, tool/function calling, model selection, and common LLM limitations.
• RAG & Retrieval: Understanding of embeddings, vector databases, semantic search, document processing, chunking, retrieval, reranking, and grounding.
• AI Application Development: Hands-on experience building applications using LLM APIs or open-source models. Familiarity with LangChain, LangGraph, LlamaIndex, or equivalent is valuable.
• Modern AI Tooling: Experience using AI coding assistants or agentic development tools to accelerate development while maintaining code quality and understanding the generated output.
• Software Engineering: Ability to write maintainable code with appropriate testing, logging, documentation, version control, and error handling.

Good to Have

• AI Agents: Experience with agentic workflows, tool-using agents, multi-step reasoning, or stateful AI applications.
• Advanced RAG: Experience with hybrid search, reranking, query transformation, metadata filtering, or knowledge graphs.
• LLM Evaluation: Exposure to evaluation datasets, LLM-as-a-judge, tracing, observability, or prompt experimentation.
• Open-Source Models: Experience with Hugging Face or other open-source model ecosystems.
• Multimodal AI: Experience with vision-language models, OCR, document understanding, or image-based AI applications.
• AI Infrastructure: Exposure to Docker, cloud platforms, CI/CD, model serving, background workers, queues, caching, or observability.
• MCP & Tool Ecosystems: Understanding or hands-on experience with Model Context Protocol (MCP) or similar approaches for connecting AI systems to external tools and data.

Mindset

• Extreme Ownership: Take responsibility for the complete feature and its outcome not just your assigned code.
• AI Curiosity: Continuously experiment with new models, frameworks, tools, and techniques.
• Engineering Over Hype: Understand that a good AI product requires reliability, evaluation, latency, cost efficiency, security, and usability not just an impressive demo.
• Bias to Action: Build quickly, test early, measure results, and iterate.
• Systems Thinking: Understand how frontend, backend, data, AI models, retrieval, infrastructure, and users fit together.
• Resourcefulness: Find practical solutions within technical, time, and infrastructure constraints.
• Continuous Learning: Stay current with rapidly evolving AI technologies and bring useful innovations into the engineering workflow.
• Product Thinking: Focus on solving real student problems rather than building technology for its own sake.

Qualifications

• Academic Standing: Final-year student with above 7.5 CGPA, recent graduate, or equivalent candidate with strong analytical and technical capability.
• Demonstrated Track Record: Portfolio of meaningful software or AI projects through GitHub, hackathons, internships, research, open-source contributions, or personal projects.
• Hands-On AI Experience: At least one practical project involving LLMs, RAG, AI agents, NLP, computer vision, recommendation systems, or another meaningful AI application.
• Full-Stack Experience: Demonstrable experience building applications involving frontend, backend, databases, and APIs.
• Problem-Solving Ability: Ability to break ambiguous problems into structured technical components and independently work toward solutions.
• Learning Velocity: Genuine interest in rapidly learning and applying new AI and software engineering technologies.

Why This Role

AI is changing how software is built, and education is one of the domains where intelligent systems can create significant real-world impact.This role goes beyond building CRUD applications or simple LLM wrappers. You will learn to engineer complete AI-native products where models interact with users, data, tools, and real-world workflows.You will gain hands-on exposure across the modern AI stack from full-stack development and RAG to agents, evaluation, multimodal AI, deployment, and production reliability while solving problems that directly affect how students learn and prepare for their careers.This is an opportunity to become an engineer who builds with AI, understands AI systems, and ships AI products.

What You'll Get

• High-Impact Stipend: ₹25,000 - ₹30,000 / month based on technical capability, execution, and demonstrated impact.
• FastTrack Career Growth: Top performers convert to a full-time role with a package of ₹6-10 LPA, based on impact and ownership shown during the internship.
• Real Product Ownership: Build features for production-oriented AI products rather than isolated internship assignments.
• End-to-End AI Engineering: Work across frontend, backend, LLMs, RAG, agents, databases, APIs, evaluation, and deployment.
• Modern AI Exposure: Work with rapidly evolving AI models, agentic systems, multimodal capabilities, and AI development tools.
• Direct Mentorship: Work closely with experienced engineers and AI practitioners to learn how production AI systems are designed, evaluated, deployed, and improved.

Our Culture

This is not an ordinary internship or a role limited to building AI prototypes. We are building intelligent systems that operate inside real warehouse and business workflows, where reliability, speed, and operational impact matter. From day one, you are expected to think like an owner, learn rapidly, build with urgency, challenge assumptions, and take responsibility for the systems and outcomes you help create.

Our Non-Negotiables

• Uncompromising Honesty : Speak directly about technical problems, limitations, failures, and risks.
• Extreme Ownership: Own outcomes, not just assigned tasks.
• Engineering Quality : AI generated code is not an excuse for poor engineering. Understand, test, review, and improve everything you ship.
• Bias to Action: Build, test, measure, and iterate.
• Relentless Learning : Continuously explore new AI models, tools, architectures, and engineering practices.
• Solve Aggressively, Ask Fearlessly : Bring proposed solutions alongside problems and raise blockers early.
• Product Impact: Build technology that solves meaningful user problems.
• Resourcefulness & Frugality : Use models, infrastructure, and engineering resources intelligently while balancing quality, latency, and cost.
• Disagree Openly, Commit Fully : Challenge decisions with evidence, debate rigorously, and fully commit once a direction is chosen.

Note :

Equal Opportunity Employer: AI.Prof is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.