▲ Builders · 6-week track

AI Engineer

The flagship Builders track. Six project-first weeks from foundations to a deployed system — RAG, agents, evals and production. You don't watch tutorials; you ship real work under working operators, then walk away with a portfolio piece and a placement pathway.

★ 4.9 · 210 reviews 6 weeks · live cohort 24 seats max Full-stack required with Mani N.

Full-stack. AI-native. Production-ready. Entry · Full Stack Assessment / Full Stack Foundation

AI Engineers are trained to build end-to-end AI applications that integrate retrieval, large language models and modern web technologies. They design intelligent systems, develop robust backends, create seamless user experiences and deploy scalable AI products in production environments.

Technical expertise

Full Stack Development
Retrieval-Augmented Generation (RAG)
LLM & SLM Applications
Vector Databases
AI Product Architecture
Model Evaluation
AI Deployment
Production AI Systems

Technologies & tools

React Next.js Node.js FastAPI PostgreSQL Pinecone LangChain OpenAI Docker

Engineers are skilled in building, integrating and deploying AI solutions using industry-leading frameworks and cloud-native tools.

What you'll ship

Four pieces of proof.

A deployed RAG system

Grounded in private data, with retrieval, vector search and citations you can trust.

A multi-agent workflow

Tool use, routing and recovery — an agent that handles a real business intent end-to-end.

An eval harness

Release gates and red-teaming — measure and harden before anything reaches production.

A portfolio project

Presented at demo day to mentors and partners — a working demo, not a certificate.

The curriculum

Week by week, project-first.

Every week ends with something shipped and reviewed. There is a mid-cohort capstone before demo day, so nothing reaches the final week untested.

Decide what belongs in the model, the application and the database — before writing the product.

The model landscape: frontier and open-weight, LLM against SLM, cost and latency trade-offs
Architecting an AI product — boundaries, state and failure behaviour
Scaffolding with Next.js, Node.js or FastAPI, and a PostgreSQL schema
Grounding your first response in real data

You ship: A running application shell returning its first grounded LLM response.

The core of most AI products in production: answers grounded in your own corpus, with citations.

Chunking strategies and embedding choices
Pinecone — indexes, namespaces and metadata filters
Hybrid search and re-ranking for answers you can defend
Citations, grounding and answering "I don't know" honestly

You ship: A RAG pipeline answering questions on your own data, with citations.

Retrieval alone is not a product. This week the interface and the intelligence meet.

LangChain tools operating inside a full-stack application
Streaming responses, optimistic UI and error states in React
Session and conversation state in PostgreSQL
The product loop: input, retrieval, action, response

You ship: Your product's core loop, working end to end.

Your product is used, not described — a mentor runs it the way a user would.

Live walkthrough and architecture review
Usability and failure review under real inputs
A prioritised plan for evaluation, deployment and hardening

You ship: A reviewed product loop and a plan to production.

Know what your system gets wrong, and how it can be misused, before your users find out.

Building an eval set — offline and online evaluation
Red-teaming: prompt injection, data leakage and jailbreaks
Regression gates on every change
Measuring quality against a baseline you can publish

You ship: An eval harness and a documented security pass on your product.

From working locally to running for other people, at a cost you can defend.

Docker, environments and secrets management
Latency, caching and cost control — moving to an SLM where it pays
Observability: tracing, logging and user feedback capture
Rate limits, abuse handling and graceful degradation

You ship: Your product deployed, instrumented and measured.

Close the gaps, write it up, and present the system to the room.

Load behaviour and failure modes under pressure
Architecture write-up and runbook
Portfolio packaging — what a hiring team needs to see
Demo day — present to mentors and hiring partners

You ship: A deployed AI product with its architecture write-up.

6 weeks at a glance

Live cohort · 24 seats · reviewed weekly by working operators.

Phase	W1	W2	W3	W4	W5	W6
AI product architecture and the frontier
Retrieval-augmented generation on private data
Agents and tool use inside the product
Mid-cohort review
Model evaluation and red-teaming
Deploy and observe
Production hardening and demo day

What learners say

From this track.

Aayush S.

AI & Full-Stack Engineer

Before TayanaFinal-year CS student

Priya R.

AI Engineer · 2 systems shipped

Before TayanaWeb developer

Kunal V.

ML Platform Engineer

Before TayanaBackend developer

Nainika P.

AI & Automation Engineer

Before TayanaQA intern

Enterprise AI solutions they can deliver

What they ship for you.

RAG-Powered Applications

LLM-Powered Applications

Intelligent AI Assistants

Vector Search Solutions

AI Product Development

AI APIs & Backend Services

Scalable Production AI Systems

For employers

Why hire AI Engineer?

Build End-to-End AI Products

Design, develop and deploy full-stack AI applications from data retrieval to production.

Leverage Modern Tech Stack

Skilled in the latest frameworks, LLM tools and cloud-native technologies.

Deliver Intelligent Experiences

Create AI-driven features and applications that enhance user experience and drive adoption.

Deploy Scalable Solutions

Build secure, reliable and scalable AI systems ready for enterprise use.

Accelerate Time to Market

Reduce development cycles with engineers who understand both AI and full-stack development.

Talk to admissions

Related Builders tracks

Keep building.

Builders career track

No-Code Agent Engineers

See the full week-by-week, what you ship, and the next cohort.

View track→

Builders career track

Pro-Code Agent Engineers

See the full week-by-week, what you ship, and the next cohort.

View track→

Builders career track

Machine Learning Engineers

See the full week-by-week, what you ship, and the next cohort.

View track→

AI Engineer

Four pieces of proof.

Week by week, project-first.

6 weeks at a glance

From this track.

What they ship for you.

Why hire AI Engineer?

Keep building.

Ready to ship?