Build AI That Thinks. Plans. Reasons. Acts.
Learn • Build • Deploy • Intern • Get Hired. The Future Belongs to AI Engineers.
Artificial Intelligence is rapidly evolving from simple chatbots to autonomous AI systems capable of planning, reasoning, collaborating, and completing complex business tasks.
Today's companies are hiring professionals who can build AI Agents, Multi-Agent Systems, AI Automation Platforms, Enterprise AI Assistants, RAG Applications, AI Copilots, and Intelligent Business Workflows.
This internship prepares students to become industry-ready Agentic AI Engineers through project-based learning, structured mentorship, and a production-style internship.
Ideal for Engineering, MCA, BCA, and MSc Computer Science Students, Fresh Graduates, Software/Python Developers, AI Enthusiasts, and Career Switchers.
We bridge the gap between traditional learning and production software engineering.
Designed using current hiring trends from startups, product companies, and enterprise AI teams.
Every concept includes Live Coding, Hands-on Labs, Mini Projects, Weekly Projects, and Production Projects.
Students work on real-world business problems in Agile teams, simulating production tasks.
Graduate with a GitHub Portfolio, Live Deployments, Technical Documentation, Professional Resumes, and LinkedIn Profiles.
Mock Interviews, Resume Building, LinkedIn Optimization, Soft Skills, Freelancing Guidance, and Startup Mentorship.
Select the program duration that matches your current baseline and career goals.
| Program | Duration | Best For |
|---|---|---|
| Program 1 | 45 Days | AI Foundations (Python, git, prompting fundamentals) |
| Program 2 | 60 Days | AI Automation (FastAPI integrations, vector datastores) |
| Program 3 | 90 Days | Agentic AI Development (LangGraph, CrewAI workflows) |
| Program 4 | 180 Days | Professional AI Engineer (Enterprise architecture, production scaling) |
We train you across every modern tool used by AI engineering teams.
Python, Git, GitHub, FastAPI, REST APIs, JSON, Docker
OpenAI GPT models, Claude Anthropic, Google Gemini, Llama, Mistral open source weights
LangGraph state charts, CrewAI agents, OpenAI Agents SDK, AutoGen workflows, LangChain
Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), Vector Databases, AI Memory, Tool Calling, Function Calling
PostgreSQL, MongoDB, Redis caching, ChromaDB, FAISS
Docker, Railway clusters, Render server, Vercel deployments, Streamlit UI dashboards
We categorize your learning outcomes into 6 core engineering competencies.
Python coding scripts, complex data structures, REST API architectures, and Git software engineering best practices.
Prompt Engineering templates, context engineering parameters, function calling payload maps, structured JSON outputs, and AI APIs integration.
Planning stages, model reasoning, reflection layers, short/long-term memory setups, tool usage schema files, and autonomous workflows.
Team AI coordination, agent collaboration channels, dynamic delegation structures, and Supervisor agents managing nodes.
Embeddings creation, text chunking heuristics, vector search models, custom knowledge bases, and hybrid semantic/keyword search.
Evaluation metrics, automated test pipelines, monitoring traces, model guardrails, security scans, and cost optimization telemetry.
Our roadmap runs on a continuous feedback cycle ensuring software engineering principles become second nature.
Real-world operational systems deployed on staging servers.
Automated screening pipeline checking resumes and extracting comparative score logs.
Conversational roadmap planner evaluating skill cards to draft mock technical review guidelines.
Dynamic file scanner organizing text folders, generating document indexing sheets.
Search bot connecting databases and internal manuals to deliver context-backed answers.
Multi-agent helper checking histories to compose responses and resolve tickets.
Autonomous researchers crawling Google Scholar and compiling comparative tables.
Audio transcript parser formulating checklist grids and assigning tasks.
OCR profile checker ranking applicant files based on target criteria matches.
Autonomous codebase checker loops on terminal syntax errors to rewrite code files.
Sync social media leads to CRM databases using webhooks and API layers.
Interactive visualization cards displaying latency logs and API costs parameters.
Autonomous state agent executing daily database operations, reporting on Slack.
Our candidates are trained to build complete AI applications, develop autonomous agent pipelines, integrate databases, deploy production-ready code, and collaborate in Agile developer teams.
A systematic onboarding pipeline built to prepare candidates for active sprints.
Bypass generic coding mockups. Secure a verified portfolio, deploy LangGraph servers, and start landing developer callbacks.
Submit your details to check qualification. Our intake sizes are strictly capped to maintain high mentor feedback cycles.