"I had been writing Flask apps at a mid-size company for two years. By Day 3, I had a multi-agent system in LangGraph that could do in seconds what our team spent weeks building manually. The capstone demo gave me the confidence to walk into my skip-level and propose our first AI product. Three months later, I lead the AI platform team."
AI-SWE INTERMEDIATE // 5-DAY INTENSIVE // 25 STUDENTS // ONSITE
Build a Production AI Platform in 5 Days. Ship It. Present It.
AI-SWE INTERMEDIATE // 5-DAY INTENSIVE // 25 STUDENTS // ONSITE
One real platform. FastAPI microservices. Production RAG. Multi-agent systems with LangGraph. Deployed on Kubernetes. Presented to a hiring panel. Built by you, alongside engineers who ship production AI for Fortune 500 companies.
Next cohort: March 2026 — 12 seats remaining
BUILT BY SQUARESHIFT — GOOGLE CLOUD PREMIER PARTNER
Broadcom
Oracle
Amazon
EY
MetLife
INTERMEDIATE
AI Software Engineering — 5-Day Intensive
You arrive as a developer who can write code but has never shipped an AI-powered production system. Over five days, you build TalentHub from scratch — a real talent management platform that uses LLMs to analyze resumes, generate interview questions, and orchestrate multi-agent workflows. You leave with a deployed, tested, monitored AI platform on your GitHub and the confidence to build the next one at work.
- TalentHub v1.0 — production AI platform on Kubernetes with FastAPI, PostgreSQL, Redis, JWT auth
- Production RAG — resume analysis with semantic chunking, hybrid search, reranking, RAGAS evaluation
- Multi-agent interview system — LangGraph 3-agent assistant with human-in-the-loop, LangSmith observability
- Full test suite — integration tests, LLM mocking, agent workflow tests, CI/CD with GitHub Actions
- Deployment pipeline — Docker to Kubernetes with auto-scaling, health checks, rolling updates
- LLM observability dashboard — token tracking, cost monitoring, latency alerts, quality drift detection
- Portfolio-ready GitHub repo — professional README, architecture diagrams, capstone demo presentation
Tech Stack
Python
FastAPI
PostgreSQL
Redis
Qdrant
LangChain
LangGraph
LangSmith
Docker
Kubernetes
GitHub Actions
OpenAI
Cursor IDE
Where You Are. Where You Could Be.
| Your Current Job | Online Courses | RocketOne Bootcamp | |
|---|---|---|---|
| What You Build | Internal tools and CRUD apps | Tutorial exercises | Production AI platform on Kubernetes |
| AI Skills | ChatGPT prompts | API wrappers and toy RAG | Production RAG + Multi-Agent + LLM-as-Judge |
| Deployment | Company servers | localhost | Kubernetes with auto-scaling and CI/CD |
| Testing | Manual QA | None | Full test suite + LLM mocking + GitHub Actions |
| Your Portfolio | Private company code | GitHub with tutorial repos | Deployed platform + Capstone demo |
| Who Guides You | Stack Overflow | Pre-recorded video | Active SquareShift engineers |
Here is how you build it. Five days. One system.
Five Days. One Production System.
Each day builds on the last. By Day 5, you have shipped TalentHub — 7 services, deployed on Kubernetes, tested, monitored, portfolio-ready.
01
DAY 1 · 9 SESSIONS
FOUNDATION — FASTAPI AND DATA LAYER
Build the backbone. Async Python, FastAPI routing, Pydantic validation, dependency injection. Wire in PostgreSQL with SQLAlchemy, add JWT auth, optimize queries, and layer Redis caching. By end of day: a fully authenticated, cached API with Swagger docs.
FastAPI Microservices + JWT Auth + PostgreSQL + Redis
Authenticated API with 5+ endpoints, async database layer, query optimization, and cache-aside pattern
FastAPI
Pydantic
SQLAlchemy
Redis
PostgreSQL
JWT
02
DAY 2 · 9 SESSIONS
INTELLIGENCE — PRODUCTION RAG SYSTEMS
Add the intelligence. Start with RAG architecture patterns, then build a resume analysis pipeline from zero — semantic chunking, embedding models, Qdrant vector database. After lunch: hybrid search combining vector similarity with BM25, reranking with cross-encoders, and quantitative evaluation with RAGAS. End with production debugging.
Resume Analysis Pipeline with Hybrid Search + Reranking + RAGAS
Full RAG pipeline — upload PDF, chunk, embed, search with hybrid retrieval, rerank, and evaluate with RAGAS metrics
LangChain
Qdrant
OpenAI
RAGAS
BM25
Cross-encoder
03
DAY 3 · 9 SESSIONS
AGENCY — MULTI-AGENT SYSTEMS
Build agency. Chain-of-thought prompting, few-shot learning, ReAct pattern for autonomous agents. Then LangGraph: graph architecture, state management, conditional routing, human-in-the-loop checkpoints. End the day with a 3-agent interview system visible in LangSmith traces.
LangGraph 3-Agent Interview System with Human-in-the-Loop
Multi-agent interview assistant — candidate research, question generation, answer evaluation — with conditional routing and full observability
LangGraph
LangSmith
LangChain
OpenAI
ReAct
CoT
04
DAY 4 · 9 SESSIONS
HARDENING — TESTING AND DEPLOYMENT
Make it production-grade. LLM-as-Judge evaluation, integration testing with pytest, LLM mocking for deterministic tests, agent workflow testing. Then containerize: Docker Compose for 6 services, Kubernetes manifests, auto-scaling with HPA, zero-downtime rolling deployments.
Docker + Kubernetes + CI/CD Pipeline + Full Test Suite
Containerized deployment with auto-scaling, ingress routing, GitHub Actions CI/CD, and comprehensive LLM test coverage
Docker
Kubernetes
pytest
GitHub Actions
HPA
Ingress
05
DAY 5 · 7 SESSIONS + CAPSTONE
LAUNCH — OBSERVABILITY AND CAPSTONE
Ship it. Wire in structured logging with Loguru, error tracking with Sentry, and LLM-specific observability — token tracking, cost monitoring, quality drift. Integrate all services. Harden for production. Prepare your portfolio. Then present TalentHub v1.0 to a mock hiring panel.
LLM Observability Dashboard + Full System Integration + Capstone Demo
Production monitoring, security audit, portfolio-ready GitHub repo, and live demo to mock hiring panel
Loguru
Sentry
LangSmith
GitHub
Portfolio
Demo
TalentHub v1.0 — 7 services. Deployed on Kubernetes. Tested. Monitored. Portfolio-ready.
Built By Engineers Who Ship. Not Academics Who Lecture.
Vikram Rao
Senior AI Engineer
"Deployed an AI-powered anomaly detection system for Broadcom last quarter. Now I teach you how to build production AI from day one."
Specialty: Full-Stack AI · Cloud Architecture · FastAPI
Clients: ['Broadcom', 'Oracle']
Priya Menon
Lead Platform Engineer
"Shipped a multi-agent pipeline for EY that processes thousands of documents. Now I teach you how to build agents that actually work in production."
Specialty: AI Agents · MLOps · Python
Clients: ['EY', 'Amazon']
Arjun Krishnamurthy
Principal Architect
"Architected cloud-native AI platforms for MetLife and Oracle. Now I teach you how to think in systems, not scripts."
Specialty: System Design · Google Cloud · Enterprise AI
Clients: ['MetLife', 'Oracle', 'Amazon']
Every instructor is an active engineer at SquareShift Technologies. They deployed production AI systems last quarter. Now they teach you how.
Train Your Engineering Team
What You Need Before Day 1
Required
- Python proficiency
- Git and GitHub
- Basic SQL
- REST API concepts
- Command line comfort
- At least one web framework (Flask, Django, Express, or similar)
Not Required
- AI or ML experience
No AI or ML experience required. You will build all of that in 5 days.
Who This Is For
Software engineers who code daily but have never shipped an AI-powered production system.
Backend engineers writing Flask/Django/Express apps who want to build AI-powered systems
Developers comfortable with Python and REST APIs looking to add production AI to their skillset
Engineers who want to deploy on Kubernetes, not just localhost
They Built It. They Shipped It. They Got Hired.
A deployed, tested, monitored AI platform on your GitHub — TalentHub v1.0
with FastAPI microservices, production RAG, a LangGraph multi-agent system,
Kubernetes deployment, and LLM observability. Plus a rehearsed capstone demo
you can show any employer. This is not a certificate. It is a working
production system in your portfolio.
Active SquareShift engineers who build and deploy production AI systems for
Fortune 500 clients. Not career instructors. Not academics. Engineers who
shipped a RAG system or an agent pipeline last quarter and will teach you
exactly how they did it.
09:00 to 17:00, five consecutive days. 43 hands-on sessions with breaks and
lunch built in. Most sessions are 45 minutes of building, not watching. You
will write code from the first hour.
Online courses teach concepts. This bootcamp ships a system. You build one
continuous project (TalentHub) across all five days — not disconnected
exercises. You deploy on Kubernetes, not localhost. You test with CI/CD, not
manual checks. And you present to a mock hiring panel, not a quiz.
Yes. The 5-day intensive covers 43 of 80 total sessions from the full AI-SWE
Intermediate curriculum. The extended course adds CrewAI, advanced system
design, full observability with Prometheus and Grafana, and a deeper capstone.
Talk to admissions for details.
Yes. Teams of 5 or more get custom cohort scheduling. Same curriculum and
intensity, delivered at your office or ours. Domain-specific capstone projects
available. Contact teams@rocketone.academy.
Yes. EMI options are available at checkout. The early bird price of INR 75,000
applies to the next cohort. Contact admissions for corporate invoicing or
custom payment plans.