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AI Engineering Accelerator

Code-first. For technical professionals.

A rigorous, code-first programme for engineers and data professionals building production AI systems — retrieval, agents, evaluation and deployment.

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Next cohort begins 19 October 2026

Next cohort · 19 October 2026

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Overview

What this programme is

The AI Engineering Accelerator is built for people who ship. You work in Python, against real APIs, with the architectural patterns behind production AI systems.

Faculty who build AI in industry take you from prototype to a deployed, evaluated and monitored system — including the parts most courses skip.

At a glance

Duration: 8 weeks
Format: Live online, code-first labs
Commitment: 2 live sessions per week + lab work
Cohort size: Capped at 16 engineers
Prerequisites: Working Python proficiency
Certificate: KIDLIN Certificate of Completion
Next cohort: 19 October 2026

Who it's for

Is this you?

Software engineers moving into AI engineering roles

Data scientists and analysts productionising LLM work

Technical founders and CTOs building AI features

Engineering leaders who need depth, not demos

Skills & outcomes

What you will be able to do

Design and ship retrieval-augmented generation systems that scale

Build multi-step, tool-using agents with reliable control flow

Evaluate models systematically instead of by vibes

Manage cost, latency and context windows in production

Implement guardrails, observability and safe failure modes

Deploy an end-to-end AI service with CI and monitoring

Curriculum

Module by module

Each module is a live working session. You learn a capability and apply it to your own work the same week.

Module 01 — LLM Foundations for Engineers

Tokenisation, context, sampling, structured output and the API surface you will live in.

Module 02 — Retrieval & Vector Systems

Chunking strategy, embeddings, hybrid search, re-ranking and grounding that actually holds up.

Module 03 — Agents & Tool Use

Function calling, planning loops, state, orchestration frameworks and failure recovery.

Module 04 — Evaluation & Reliability

Offline and online evaluation, golden datasets, regression suites, guardrails and red-teaming.

Module 05 — Production Engineering

Cost and latency engineering, caching, streaming, observability, versioning and rollout.

Module 06 — Capstone System

Ship a deployed AI service end to end with an architecture review from practising AI engineers.

Tools covered

What you will work with

PythonAPIsLangChainVector DatabasesClaudeOpenAIAI Agents

What's included

Everything in your cohort

16 live labs and architecture sessions

Reference codebases and starter repositories

Code review on your capstone system

Weekly engineering office hours

Private engineering cohort channel

Deployment and evaluation playbooks

Faculty

Who teaches this programme

Mayank Singhal

System Software Engineer, NVIDIA

IIT graduate with expertise in AI, data science, cloud infrastructure and large-scale software systems.

Sarthak Srivastava

Founder & CEO, VANCO.AI

Former AI Engineer at NVIDIA and Virginia Tech faculty member, specialising in AI, cloud computing and enterprise AI deployment.

Learner stories

From people who did it

What stood out was how hands-on it was. Instead of just talking about AI, we actually built things and saw how the tools work in real situations. I walked away with ideas that save me hours every week.

Gita D.

Working Professional

I have watched dozens of AI videos online, but this was the first time I actually understood how to use AI in my day-to-day work. I left with tools and workflows I could apply immediately.

Tejass V.

Product Manager