Become an Applied AI Engineer
12 AI systems. Real feedback. Applied AI portfolio. Remote cohort.
Who Is This For
- Backend Engineers
- Fullstack Developers
- Data Scientists
What You Will Achieve
- ✓Build 12 Production AI Systems
- ✓Master RAG and Vector Databases
- ✓Create an Applied AI Portfolio
- ✓Move from 'Prompt Engineer' to 'AI Engineer'
Frequently Asked Questions
- Does this guarantee a job?
- No. It builds competence and confidence — and that wins Applied AI Engineer roles in India and remote.
- No strong backend. Right for me?
- Yes. Python or Node.js and basic API knowledge are enough. Willingness to build — we guide the rest.
- Can I use my language or framework?
- Yes. Specs define behaviour. Python, Node.js — your choice. We focus on patterns, not one stack.
The AI Engineer Accelerator
Build 12 Production AI Systems. Master Agents, RAG & Voice.
Become an Applied AI Engineer
12 AI systems. Real feedback. Applied AI portfolio. Remote cohort.
Lifetime Access • Interview-Ready Portfolio
Trusted by Engineers at Leading Product Companies
Which one hurts the most?
Verified 'Prompt Engineer'?
Anyone can write a prompt. Companies hire engineers who build reliable systems.
Demos don't scale
Your RAG works on a PDF. It breaks on a database. You need to learn production pipelines.
Agents sound exciting — you don't know where to start
Tools, routing, multi-step. Design, test, scale? Tutorials stop at demos. You want something that lasts.
"Prompt Engineer" — but you want to be an AI Engineer
Companies hire people who build agents and pipelines. You want the skills and portfolio.
Companies promote people who build AI systems, not just prompts. We get you there.
The gap isn't your prompts.
It's the systems reps you never got.
Senior engineers aren't better because they know more syntax. They're better because they've:
Tutorials don't give you that. We simulate it. You earn it. You prove it.
Not a course.
An Applied AI Engineer simulation.
PRD → design → build → review. RAG, agents, evaluation. Real feedback. Same bar as a real team.
The 4-Step Workflow
Prompts first. Systems second.
Product Spec (PRD)
Vague requirements. Accuracy and latency limits. Like a real product.
Design Doc (RFC)
Design before code. Models, chunking, tools, eval. Document trade-offs. Defend choices.
Implementation
Build it. Hallucinations, retries, cost. No shortcuts. Production-ready from day one.
Code Review
Open a PR. Feedback on architecture, correctness, latency, cost. Iterate until production-grade.
12 AI systems in 12 weeks — RAG, agents, and production-grade patterns
Module 1: Foundations (Structured AI)
Stop Chatting. Start Engineering.
Structured Extraction with LLMs
The "Stripe" ParserBuild a pipeline that turns messy receipt images into perfect, type-safe JSON using Zod schemas.
RAG & Vector Embeddings
The "Chatbase" BotCreate a support bot that answers questions from custom PDF documents without hallucinations.
function Calling & MCP
The "Glean" ConnectorBuild a system that connects to Google Drive to search and indexes files via Model Context Protocol.
Long-term Memory & Context
The "ChatGPT" MemoryImplement a 'Memory OS' that remembers user preferences across sessions using vector stores.
Module 2: Agentic Workflows
Agents that actually work.
Intelligent Routing Agents
The "Zapier" RouterBuild a router that analyzes intent and dynamically selects the right tools and workflows.
Multi-Step Enrichment Chains
The "Clay" EnricherCreate a sales agent that researches leads by scraping web, LinkedIn and news sequentially.
Voice AI & Real-time Streams
The "Vapi" ReceptionistBuild a real-time voice receptionist that handles calls, books appointments and manages interruptions.
Browser Use & Computer Use
The "Kayak" BrowserCreate a computer-use agent that can navigate flight websites to find and book tickets.
Module 3: Production Architect
Scalable, Evaluated, Reliable.
Human-in-the-Loop Systems
The "Travel" HITLArchitect a complex booking workflow where AI drafts actions but humans approve high-stakes decisions.
Data Engineering for AI
The "Fivetran" ETLBuild robust data pipelines that clean, chunk and ingest massive datasets for RAG systems.
Text-to-SQL & Analysis
The "Tableau" AnalystCreate an agent that can write safe SQL to query production databases and generate charts.
Reasoning Models & Capstone
The "CodeRabbit" CloneBuild an autonomous code review agent that provides line-by-line feedback on GitHub PRs.
See everything first
Full curriculum — every system, every week — before you commit.
Prompts are easy. Systems are hard.
AI can run prompts.
Chat, extraction, boilerplate.
AI cannot:
- Design production RAG pipelines
- Connect agents to tools and guardrails
- Define eval and improve over time
- Own cost and latency
That's what AI engineers do. We train you.
Learn From Someone Who Actually Does This
I am the Head of Engineering at Jobbatical (EU Tech), with 8+ years of leadership and 15+ years of total experience in the software industry.
"Most engineers are not blocked by ability, but by lack of real system ownership."
This accelerator exists to give you what most jobs never will.
Guest Sessions From Engineers at
Live sessions on System Design, Career Growth, and Interview Preparation.
Join the Accelerator
Invest in your career. It pays back 100x.
Accelerator Program
Cohort-based live learning
- Everything in Self Study
- Private Community AccessPremium Support with Code Reviews
- Async Mentorship
- Live: Weekly Q&A
- Live: Group Code Reviews
- Personalized Career SupportNetworking Opportunities
- Premium Workshops Access
Bonus Resources
The "why" behind AI systems. Ship with confidence.
Learn the Why Behind AI Engineering
RAG, agents, evaluation. Reasoning for interviews and production.
Solution Walkthroughs
Step-by-step for every challenge. Multiple approaches. Trade-offs explained.
FAQ
Does this guarantee a job?
No. It builds competence and confidence — and that wins Applied AI Engineer roles in India and remote.
No strong backend. Right for me?
Yes. Python or Node.js and basic API knowledge are enough. Willingness to build — we guide the rest.
Can I use my language or framework?
Yes. Specs define behaviour. Python, Node.js — your choice. We focus on patterns, not one stack.
12 weeks from now:
Titles don't make AI engineers. Shipped systems do.