Python learners
Ready to move from syntax into real AI product engineering.
Offline Intensive Program - Ahmedabad
Narvix AI Engineering Program 2026
AI Engineer
Build Real AI Systems. Deploy With Confidence.
Backend - Cloud - Agents - RAG - Deployment - Production Workflows
Input Layer
documents / users / tools / APIs
FastAPI
Auth + DB
RAG Engine
Agent Tools
$ deploy --env production
OK vector index mounted
OK agent workflow ready
OK monitoring enabled
The Shift
AI training has split into two categories.
Old Pattern
Prompt-only learning
- Tutorial culture
- Tool usage without systems
- Demo outputs without deployment
- No engineering workflow
Narvix Direction
AI systems engineering
- Backend-integrated AI systems
- Deployment and cloud workflows
- RAG, agents, orchestration
- Production execution discipline
Narvix trains for the second category.
Who Should Join
Built for serious learners who want execution depth.
This program is designed for serious learners willing to build consistently.
Developers
Transitioning into LLM systems, cloud workflows, and agentic AI.
Engineering students
Seeking portfolio-grade systems rather than academic-only exercises.
Builders
Prefer practical execution, debugging, iteration, and shipping.
Learning Roadmap
A clear path from foundation to deployed AI systems.
1. Python Engineering Foundation
Advanced Python, OOP, AsyncIO, and professional project structure using Poetry and Git.
2. Production Backend Systems
High-performance APIs with FastAPI, background workers with Celery/Redis, and SQL/NoSQL architectures.
3. AI Systems Architecture (RAG)
Vector database integration, embedding models, and building production-grade Retrieval Augmented Generation pipelines.
4. Cloud & Deployment Systems
Cloud fundamentals, GCP, hosting APIs and web apps, Docker, and CI/CD workflows.
5. LLM Engineering
OpenAI, Gemini, Vertex AI, prompts, evaluation, and quality thinking.
6. Agentic Systems
Function calling, tools, agents, memory, MCP, and multi-agent workflows.
7. Production Capstone Build
Build, review, and deploy a real AI system from scratch.
Phase Breakdown
Structured like an engineering track, not a topic dump.
Engineering Foundation Sprint
Python revision, CLI workflow, functions, modules, collections, exception handling, debugging, JSON, files, and terminal AI tools.
Outcome: reliable development habits before advanced AI work.Software Engineering Core
OOP, multithreading, multiprocessing, async concepts, design patterns, clean architecture, and maintainable code.
Outcome: stronger structure and scalable system thinking.Backend Engineering
REST APIs, FastAPI, Pydantic, auth, databases, logging, error handling, frontend basics, and backend + UI integration.
Outcome: backend services ready to power AI products.Industry Workflow Simulation
GitHub workflows, branches, pull requests, Jira, Agile basics, documentation, code reviews, and iteration mindset.
Outcome: students learn how engineering teams actually work.Cloud & Deployment Systems
Cloud fundamentals, GCP, hosting APIs and web apps, Docker, Compose, CI/CD, secrets, environments, and monitoring.
Outcome: apps move from laptop to hosted environments.LLM Systems Engineering
LLM fundamentals, OpenAI, Gemini, Vertex AI, prompts, embeddings, chunking, vector stores, RAG, orchestration, and evaluation.
Outcome: usable LLM systems with retrieval and quality thinking.Local Models & Agentic AI
Function calling, tools, agents, memory, MCP, automation workflows, n8n-style tools, local models, LoRA, and fine-tuning vs RAG.
Outcome: students move beyond simple chatbots into task systems.Production Capstone Build
Students build, review, and deploy a real AI system such as a document assistant, support bot, automation agent, or internal LLM tool.
Outcome: a portfolio-grade shipped project.Industry Workflow Simulation
Students practice the workflow around the code.
Tasks, implementation challenges, deadlines, bug-fix requests, feature requests, reviews, and deployment cycles simulate real engineering pressure.
Git
workflow
Pull requests
Jira tasks
Code reviews
Debugging
Deployment cycle
Documentation
Iteration mindset
Tools & Stack
A practical ecosystem for modern AI engineering.
Engineering
Python, FastAPI, Pydantic, GitHub, REST APIs
Deployment
Docker, Docker Compose, GCP, CI/CD, secrets, monitoring
LLM Systems
OpenAI, Gemini, Vertex AI, LangChain, embeddings, RAG
Agents & Local AI
Ollama, ChromaDB, MCP, n8n, local model APIs
Projects
Production-oriented builds, not academic dataset exercises.
01
AI Research Assistant
Search, summarize, cite, and organize research workflows with LLM APIs.02
AI Document Assistant
RAG pipeline for documents, chunking, embeddings, retrieval, and answers.03
Multi-Agent Workflow System
Tool calling, multi-step execution, state, memory concepts, and review loops.04
Business Automation Agent
Workflow automation with APIs, tasks, approvals, and no-code integrations.05
Internal AI Knowledge System
Private knowledge assistant with permissions, retrieval, and deployment.06
Customer Support AI Bot
Backend-integrated support assistant with escalation and evaluation thinking.Why Narvix
Controlled confidence. No hype.
Direct mentoring model
Production-oriented training
Build-first learning
Cloud + local AI exposure
Mandatory capstone project
Certification Standard
Certification is earned through execution.
Completion is not attendance alone. Students must maintain serious participation, complete practical tasks, finish milestones, ship at least one working capstone, and demonstrate project understanding during review.
Outcomes
Closer to junior engineer expectations.
- Build backend AI systems
- Use LLM APIs professionally
- Create RAG pipelines
- Build and deploy AI agents
- Use Git workflows and task systems
- Present a stronger technical portfolio
Narvix Standard
We do not train passive learners. We build capable engineers.
Engineering Training Institute