20 Weeks

80+ hours of Learning

AI Engineer
(Gen AI)

Professional Certification Program

Learn ML, Deep Learning, Multi-Agent Systems, and LLMOps while building real-world projects like an Agri-Tech Multi-Agent System and Uber demand prediction model.

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& 200+ Others joined

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Wanna get hired as an Al Engineer? Must Watch

Our Impact

100+

Paid Job Placements in the past 8 months

5000+

Learners have enrolled in our bootcamps and succeeded

80%

of the learners who succeeded with us are beginners

Our grads get great jobs with both startups and industry giants

Become a Job-ready AI Engineer
with STEM Link

Who is an AI Engineer?

🤔Did you know, most fast-growing tech companies are
searching for industry-ready AI Engineers?

AI Engineers design and build production-ready AI systems that solve real business problems. This involves working with Large Language Models (LLMs), building RAG systems, developing AI agents, and deploying scalable solutions to the cloud. An AI Engineer bridges the gap between experimental AI research and production systems that can handle enterprise-scale workloads.

How we Accelerate
your Job Placement

Top performers in our program hired within

3 Weeks to 2 Months

Average CS Graduate hired within

6 Months to 1 Year

The learning experience designed
to make you an AI Engineer

From day one, you build, debug, deploy, and present real AI systems with mentor guidance and career preparation built into the program.

Build AI Systems from Day 1 of the Bootcamp

Start building AI systems early in the program, from ML models to agentic applications that solve real-world workflows.

Get mentored by Top-class AI engineers

Learn with guidance from engineers, architects, and practitioners who build real systems.

Solve real-world AI case studies

Work through industry-inspired projects like Uber-style demand prediction and Microsoft FarmVibes-based Agri-Tech intelligence.

Forecast ride demand using real-world mobility patterns.

Build agri-intelligence using Microsoft's open-source FarmVibes.

Become interview-ready before graduation

Practice mock interviews, sharpen your CV, and learn to explain your AI projects with confidence.

If this sounds like you, this program is built for you

You know programming basics

You can write basic code, understand logic, and are ready to work with Python, APIs, and AI engineering workflows.

You're a software engineer moving into AI

You already build applications, but now want to add LLMs, RAG, agents, ML, and LLMOps to your skillset.

You don't want to be stuck in theory

You want to build industry-grade AI apps, solve industry problems, and graduate with portfolio-ready projects.

You After the AI Engineer
Program

Shane Fernando

Portfolio

AI Expertise

Loop Engineering

LLMOps

Multi-Agent

Deep Learning

Memory Management

RAG

Fine Tuning

Education

AI Engineer Professional Certification

Jul - Nov 2026

Capstone Project

Ride Demand Prediction Engine

Agri-Tech Agentic Intelligence Platform

AI Engineering Skills & Tools

OpenAI

HuggingFace

LangChain

LangGraph

LlamaIndex

Pinecone

FAISS

FastAPI

PyTorch

scikit-learn

Docker

Langfuse

Helicone

Google Cloud Platform

Python

PostgreSQL

Git

How you get an AI Engineer
Role in just 4 Months

Master LLM foundations, RAG systems, and AI agent development with industry-standard tools.

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Build production-ready AI applications from prompt engineering to cloud deployment.

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Career coaching and building a compelling CV and AI project portfolio for engineering roles.

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Get You Hired

Practical AI Engineering
Mastery with Industry Tools
& Modern Frameworks

Mastering LLMs &
RAG Systems

Hands-on with Tools:
LangChain, FastAPI, AutoGen

Strong Understanding of
AI Agents, LLMOps, and
Production Deployment

This is how your
Tech Stack would look like

Student working on practical AI engineering modules

New additions to the AI Engineer Bootcamp

We’ve added new practical modules to help you move from building basic AI apps to engineering reliable, production-ready AI systems.

Loop Engineering

Context Engineering

Terraform-Based Deployment

Master Transformer Architecture and Local Intelligence

Dive into transformer architecture, self-attention, embeddings, and positional encoding, and trace the evolution of NLP and training paradigms like pre-training and in-context learning. Move beyond the cloud to deploy open-source models such as Llama or Gemma on local hardware with Hugging Face and GGUF, then master quantization to cut VRAM usage and PEFT with LoRA to customize behavior.

What you will do in practice:

Analyze the mathematical intuition behind self-attention and positional encoding.

Evaluate Byte Pair Encoding and how tokenization shapes model input.

Deploy and quantize open-source models locally with Hugging Face.

Build a high-throughput local inference environment as your Level 1 project.

Software and languages you will learn:

PyTorch

Llama

HuggingFace

Build End-to-End Machine Learning Foundations

Master the core ML lifecycle that sits underneath every modern AI system. Turn raw datasets into clean, training-ready formats with NumPy and Pandas, then work through the three pillars in order: supervised learning and the metrics that prove reliability, unsupervised learning for structure discovery, and deep learning basics in PyTorch, relating neural weights and biases to modern embeddings.

What you will do in practice:

Clean, transform, and prepare datasets with NumPy and Pandas.

Apply regression, classification, and clustering with scikit-learn.

Judge models with Precision, Recall, and Confusion Matrices.

Deliver a Predictive Classification and Segmentation System as your Level 2 project.

Software and languages you will learn:

NumPy

Pandas

scikit-learn

PyTorch

Engineer Reliable Context for Production Models

Move beyond basic prompting into context engineering, the skill that drives reliable model behaviour in production. Write reusable context and rules files, budget the context window, and design structured, schema-bound outputs, then apply a disciplined spec-driven build loop with guardrails and lightweight evaluation using AI-assisted tools.

What you will do in practice:

Apply roles, constraints, few-shot, and chain-of-thought with intent.

Write reusable context and rules files and produce reliable JSON outputs.

Run a spec-to-build loop with guardrails and a lightweight rubric.

Build a reusable Context Pack and AI-powered feature as your Level 3 project.

Software and languages you will learn:

Cursor

Lovable

OpenRouter

Build Production-Grade RAG and Knowledge Management

Master the distinction between passive RAG and autonomous agents, then build advanced retrieval pipelines with LangChain and LlamaIndex. Overcome hallucinations through semantic chunking, hybrid search, and cross-encoder reranking, and scale with Pinecone, culminating in CodeOracle, a production-grade Intelligent Knowledge Management System.

What you will do in practice:

Build ingestion pipelines with semantic chunking and overlapping strategies.

Populate FAISS and Pinecone vector stores with embedded technical data.

Route developer queries across source code, docs, and git logs.

Deliver CodeOracle, a production-ready IKMS API, as your Level 4 project.

Software and languages you will learn:

LangChain

LlamaIndex

Pinecone

FAISS

Harden AI Systems Against Adversarial Attacks

Adopt the mindset of a red teamer to uncover hidden weaknesses, performing prompt injection and adversarial attacks to test resilience. Implement multi-layered guardrails and semantic filters with Guardrails AI, audit data-leakage risks, and deliver a hardened CodeOracle secured against malicious manipulation and aligned with ethical AI standards.

What you will do in practice:

Perform prompt injection and adversarial attack simulations.

Audit data-leakage risks across your AI tools and context.

Implement guardrails and semantic security filters.

Deliver a hardened CodeOracle with documented defenses as your Level 5 project.

Software and languages you will learn:

OpenAI

Guardrails AI

Customize LLMs with PEFT, LoRA, and QLoRA

Evaluate the trade-offs between dynamic retrieval (RAG) and internalized reasoning (fine-tuning), then transform your RAG knowledge base into high-quality instruction datasets. Apply Parameter-Efficient Fine-Tuning with LoRA and QLoRA to specialize models without heavy compute, track training with Weights & Biases, and expose the result through production REST APIs.

What you will do in practice:

Decide when to fine-tune versus rely on RAG for domain adaptation.

Apply LoRA and QLoRA with Hugging Face fine-tuning libraries.

Track loss and evaluation metrics with Weights & Biases.

Deliver a fine-tuned CodeOracle variant as your Level 6 project.

Software and languages you will learn:

HuggingFace

PyTorch

Weights & Biases

Orchestrate Multi-Agent Systems with LangGraph and MCP

Architect true multi-agent systems where each agent has its own model, prompt, and memory, coordinated through orchestrator-worker, supervisor, and hierarchical patterns. Add an Agent Control Plane, an agent harness, and resilience patterns, then wrap agents in FastAPI and Gradio with real-time streaming and connect external systems through MCP servers built with FastMCP.

What you will do in practice:

Compare orchestration patterns and select the right one per workflow.

Stream agent and tool outputs live through FastAPI and Gradio.

Build MCP servers for secure access to databases and file systems.

Deliver a Multi-Agent Travel Planner as your Level 7 project.

Software and languages you will learn:

LangGraph

ConvexDB

FastAPI

Gradio

FastMCP

Scale AI Systems with LLMOps and Cloud Deployment

Bridge prototype to enterprise-grade product. Containerize your AI stack with Docker, architect high-performance FastAPI services, and deploy to AWS, GCP, or Azure with KV-cache quantization for high-concurrency performance. Configure observability with Langfuse and Helicone to monitor latency, token cost, and drift, and gate releases so only validated builds reach production.

What you will do in practice:

Containerize AI agents with Docker for reproducible deployments.

Deploy scalable FastAPI services to AWS, GCP, or Azure.

Implement security guardrails, rate limiting, and release gates.

Deliver a Scalable AI Agent Deployment as your Level 8 project.

Software and languages you will learn:

FastAPI

Docker

Kubernetes

GCP

Langfuse

Helicone

Apply AI to Enterprise Demand Prediction

Follow one enterprise system end to end through a case study on demand prediction in ride-hailing. See how a platform like PickMe or Uber forecasts where and when riders need cars, prices those rides dynamically, and then actively shapes demand, framing demand as a forecasting problem driven by pricing, timing, and local traffic.

What you will do in practice:

Frame demand prediction as a time-series forecasting problem.

Identify the features that drive accurate demand forecasts.

Connect predictions to dynamic and surge pricing.

Measure forecast accuracy against real outcomes.

Software and languages you will learn:

scikit-learn

PyTorch

Pandas

Become Job-Ready with Portfolio and Interview Prep

Transition from AI Engineering student to job-ready professional. Build a standout CV and a structured GitHub portfolio that tells the story of your technical growth, submit your materials for individual expert review, and prepare for behavioral and technical interviews with real question frameworks and targeted application strategies.

What you will do in practice:

Build a professional AI Engineer CV from curated templates.

Structure a GitHub portfolio around your LLM, RAG, and agent projects.

Submit your CV for individual review and feedback from experts.

Prepare for behavioral and technical interviews with real frameworks.

Software and languages you will learn:

GitHub

Master Transformer Architecture and Local Intelligence

What you will do in practice:

Analyze the mathematical intuition behind self-attention and positional encoding.

Evaluate Byte Pair Encoding and how tokenization shapes model input.

Deploy and quantize open-source models locally with Hugging Face.

Build a high-throughput local inference environment as your Level 1 project.

Software and languages you will learn:

PyTorch

Llama

HuggingFace

Download Full Curriculum

25 pages

Top-notch curriculum crafted by Sri Lanka's leading AI engineers with over a decade of experience, ensuring you're job-ready from the start.

Download Curriculum

Preparing You for the Industry
by Bringing in the CxOs of
Largest Firms

We have brought in the Chief Technology Officers of leading tech hubs like OREL, Surge, and Inivos to train you to thrive in the industry. The network you get here is unmatchable.

Build AI systems inspired by real-world industry use cases.

Inside the bootcamp, you won't stop at simple chatbots. You'll build practical AI engineering projects that combine LLMs, RAG, agents, APIs, databases, prediction models, and deployment workflows.

Multi-Agent Agritech Intelligence System

Project FarmVibes

Multiple agents analyze crop conditions, retrieve agricultural knowledge, check external data, and generate practical recommendations.

Dynamic Demand Prediction Engine

Demand Prediction

Build a machine learning system that predicts demand using historical data, location, time, weather, availability, and user activity patterns.

Meet Coach Jordan — your AI career coach for tech CVs.

3x stronger ATS alignment

Job-specific CV generation

Conversational CV experience

Built for tech hiring

Coach Jordan is STEM Link's custom-built CV preparation tool, designed specifically for tech careers. It helps students create job-specific, ATS-friendly CVs through a conversational CV-building experience.

Your career support doesn't stop at graduation.

Bi-weekly personal coaching call

After graduation, students join a career coaching session once every two weeks on Thursday to review progress, stay accountable, and prepare for opportunities.

CV & portfolio preparation

We help you convert your bootcamp projects into strong CV, LinkedIn, GitHub, and portfolio content that communicates real AI Engineering capability.

Mock interviews & gap fixing

Before real interviews, we run mock interviews, evaluate your answers, and help you fix weak areas in your technical explanations and project knowledge.

400+ learners supported

Across STEM Link programs, our career coaching process has helped hundreds of learners prepare, apply, interview, and move into tech roles.

Will you be certified?
Of course! It'll look great
on your resume and LinkedIn

Software Engineering Professional Certificate

Learn Directly from Leading
AI Engineers & Career
Development Coaches

Our mentors are industry practitioners and career specialists with deep expertise in AI Engineering, Machine Learning, and career growth.

Main Track Mentor

Sachith

Senior AI Engineer at Gapstars, ML Engineer at Surge Global

Main Track Mentor

Dinesh

AI Research Executive at SLT Mobitel

Sub Track Speaker

Poornima

Technical Lead at 99x

Sub Track Speaker

Roy

Principal Architect at PickMe

Sub Track Speaker

Pasindu

Founder Zeya Health, Vice President Surge Global

Sub Track Speaker

Ashan

Senior Tech Lead at Sysco LABS

Sub Track Speaker

Vidura

AI Project Lead at IFS

Sub Track Speaker

Sabilashan

Ambassador and Creator at Lovable

Sub Track Speaker

Manupa

Software Engineer (AI) L3 at Taxxa.ai

Sub Track Speaker

Dilshara

Technical Consultant/CEO at STEM Link

Sub Track Speaker

Tharana

Head of Engineering at STEM Link

Don't listen to us. Listen to their success stories

"Thanks to STEM Link's coaching and mock sessions, I cleared 7 interviews and landed an ML Engineer Internship at.."

Chalindu Yohan

7:45