GyanHQ Super30

Become Job-Ready for the
New AI Engineering Roles

Live Super30-style cohort training for AI Engineers, Agent Engineers and Forward Deployed Engineers. Not a course. Not a tutorial. A selective, mentor-led, 16-week programme built around real production engineering.

16 Weeks Live Cohort
30 Seats Per Batch
8 Hands-On Phases
5 Target Roles
Apply for Next Super30 Batch →

Why This Programme Exists

GyanHQ bridges the gap between AI theory taught in colleges and real engineering work inside modern companies.

What we are NOT

  • An AI tutorial website
  • A self-paced course portal
  • A course marketplace
  • A place to learn prompting
  • A demo-building bootcamp

What we ARE

  • An elite mentorship community
  • A live, mentor-led classroom
  • A selective cohort programme
  • A job-readiness academy
  • A Super30 for AI Engineering

Primary Outcome

  • Build production AI systems
  • Deploy to real cloud infrastructure
  • Operate with reliability + safety
  • Communicate ROI to stakeholders
  • Ship — not just learn

Target Roles

Students are prepared for these high-demand industry roles at Google Cloud, OpenAI, Anthropic and Scale AI.

AI Engineer

Builds production AI applications using LLMs, RAG, and structured outputs.

LLMs Prompt Engineering RAG Function Calling Evaluation Production Deployment

Agent Engineer

Builds multi-agent systems with planning, memory and orchestration.

Agent Architecture MCP Tool Calling Agent Memory Multi-Agent Orchestration Agent Security

Forward Deployed Engineer

Core Focus

Works directly with customers — from discovery to production delivery.

Requirement Discovery Solution Architecture Rapid Prototyping Production Delivery Customer Enablement ROI Communication

AI Platform Engineer

Builds internal AI platforms with Kubernetes, GPUs and observability.

Kubernetes Cloud Run GKE GPU Infrastructure Observability Governance

AI Infrastructure Engineer

Responsible for model deployment, scalability, reliability and cost optimisation.

Terraform GitOps Networking Service Mesh Cost Optimisation Reliability

16-Week Learning Journey

Eight structured phases. Live classroom. Hands-on labs every week. Mentor reviews throughout.

01 Weeks 1–2

Engineering Foundation

Build real software, not just run AI prompts. Python, TypeScript, REST APIs, Docker, Git and full-stack basics.

Outcome Build and run a basic full-stack AI application.
02 Weeks 3–4

LLM Application Engineering

Build useful applications using LLMs. Prompt engineering, structured outputs, function calling, token limits and cost awareness.

Outcome Build LLM-powered applications with predictable output.
03 Weeks 5–7

RAG Engineering

Build enterprise knowledge assistants. Embeddings, vector databases, hybrid search, re-ranking, citations and hallucination reduction.

Outcome Build and evaluate a production-style RAG system.
04 Weeks 8–10

Agent Engineering

Build agentic workflows, not just chatbots. ReAct pattern, multi-agent systems, agent memory, human-in-the-loop and tracing. LangGraph, CrewAI, Google ADK.

Outcome Build controlled, observable agent workflows.
05 Weeks 11–12

MCP & Enterprise Integration

Connect AI to real enterprise systems. MCP servers, API integration, authentication, database and SaaS integration, security boundaries.

Outcome Connect AI systems to real business systems via MCP.
06 Weeks 13–14

Cloud & Production Deployment

Move from demo builder to production AI engineer. Cloud Run, Kubernetes, CI/CD, Terraform, observability, structured logs and cost-per-request.

Outcome Deploy and operate an AI application in cloud.
07 Week 15

AI Evaluation, Safety & Governance

Prove AI is safe and useful. Eval datasets, prompt regression testing, guardrails, PII handling, audit logs and AI risk registers.

Outcome Prove an AI system is working, safe and measurable.
08 Week 16

Forward Deployed Engineering Simulation

A mock customer gives a real business problem. Students conduct discovery, capture requirements, define success metrics, build prototype, deploy MVP and present to stakeholders.

Outcome Operate as a junior Forward Deployed AI Engineer.

Capstone Projects

Every student completes one capstone — a fully deployed, production-grade AI system.

01

Enterprise RAG Assistant

Secure document assistant with ingestion, embeddings, retrieval, citations, evaluation and deployment.

02

Multi-Agent SDLC Assistant

Agents for requirement analysis, design review, code generation, test generation and deployment support.

03

Customer Support AI Agent

AI support agent connected to FAQ, ticketing system, API tools and escalation workflow.

04

Audit Evidence Agent

Agent that collects, validates and summarises compliance evidence from multiple enterprise systems.

05

Data Analysis Agent

AI agent that connects to structured data, answers business questions and generates executive reports.

What You Graduate With

Every Super30 graduate leaves with a complete, verifiable portfolio.

GitHub repository with working code
Working deployed AI application
Architecture diagram
Technical design document
Evaluation and safety report
Demo video
LinkedIn project post
Resume project description
"I can discover a business problem, design an AI solution, build a prototype, deploy it to cloud, evaluate its quality, connect it to enterprise systems, explain the ROI, and support adoption." — The Super30 Graduate Profile

30 seats. One cohort. Zero shortcuts.

Ready to Ship AI in the Real World?

We review every application by hand. If you're serious about becoming a production AI engineer — apply now.

Apply for Next Super30 Batch → Meet the Instructor