Intake open · 3-month track

Deep Learning Internship

Build neural networks that solve an actual problem: trained, tuned, evaluated honestly, and deployed behind an inference endpoint.

Apply now See the 12-week plan
3Months
HybridFormat
498+Enrolled
FreeTo apply
Classifier. Epoch 24
# fit and validate
model.fit(X_train, y_train)
score = evaluate(model, X_val)
Validation accuracy94.2%
epoch 1epoch 10
Trained with
PyTorch + GPU
Mentor review
Weekly, 1-to-1
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
  • PyTorch
  • CNNs
  • Transformers
  • Transfer Learning
  • Colab
  • ONNX
12Weeks
25 hrsPer week
498+Interns trained
1Trained model
The work

Anyone can copy a training loop. Few can debug one

Your loss will plateau, your validation will diverge, your model will memorise the training set. Learning to diagnose which of those is happening is the actual skill.

  • 1You start from a pretrained model, not from scratch
  • 2You build a clean data pipeline with real augmentation
  • 3Your mentor reviews the training curves with you
  • 4You export, quantise and serve the final model

Weeks 1 to 3 cover Python, NumPy and neural network fundamentals from zero.

train.py
# Week 7: transfer learning, mixed precision, early stop
model = timm.create_model("resnet50", pretrained=True,
                          num_classes=7).to(device)

opt    = torch.optim.AdamW(model.parameters(), lr=3e-4,
                              weight_decay=0.05)
sched  = torch.optim.lr_scheduler.OneCycleLR(
             opt, max_lr=3e-4, total_steps=epochs * len(train_dl))
scaler = torch.cuda.amp.GradScaler()

for epoch in range(epochs):
    model.train()
    for x, y in train_dl:
        opt.zero_grad(set_to_none=True)
        with torch.cuda.amp.autocast():
            loss = criterion(model(x.to(device)), y.to(device))
        scaler.scale(loss).backward(); scaler.step(opt); scaler.update()
        sched.step()
Skills

Eight things you will actually be able to do

01

Python and PyTorch

Tensors, autograd, modules and writing a training loop you understand.

02

Neural network theory

Backpropagation, activations, loss functions and what gradients actually do.

03

CNNs

Convolutions, pooling, architectures and why they work on images.

04

Transformers

Attention, embeddings and applying pretrained language models to real text.

05

Transfer learning

Fine-tuning pretrained weights instead of burning a week training from scratch.

06

Data pipelines

Datasets, loaders, augmentation and preventing train/test contamination.

07

Training diagnostics

Reading loss curves, spotting overfitting, and fixing a run that is not learning.

08

Inference and deployment

Export to ONNX, quantisation, batching and serving predictions under load.

Curriculum

The 12-week plan

Four phases. Each one ends in a deliverable that is reviewed and graded before the next opens.

Foundations

Python, tensors and your first network.

  • Python, NumPy and array thinking
  • Tensors, autograd and gradient descent by hand
  • A simple network trained end to end on real data
  • Colab, GPUs, environments and reproducibility
Deliverable

A neural network you wrote and trained yourself, with a documented loss curve and evaluation.

The capstone

Every intern trains and ships one real network

You take an Ezitech problem (document classification, defect detection, ticket routing), and carry it from raw data to a served model with an honest evaluation.

  • A model fine-tuned on real, imperfect data
  • Training curves and error analysis you can defend
  • A served endpoint with a measured latency budget
Start your application
Ezitech seminar session with students at a university auditorium
Ezitech stall at a university open house and job fair
Ezitech receiving a shield at a university project presentation
Ezitech team and interns group photo outside the institute
Ezitech speaker presenting at a talent award ceremony
Ezitech mentors reviewing intern project documents
Ezitech team discussing an intern project brief in the office
01 / 07
Duration12 weeks
Commitment25 hrs / week
FormatOnsite + remote
LocationRawalpindi, PK
LevelJunior / fresher
ReviewsWeekly, 1-to-1
The Ezitech model

What every Ezitech intern gets

Three things that make this different from a course you watch on a screen.

Live client briefs

You work on real Ezitech projects with real deadlines and real revision rounds, not recycled practice exercises.

A mentor, not a playlist

Weekly reviews with someone who does this work for a living, plus a team of interns around you to build with.

Portfolio and experience letter

You finish with shipped work you are allowed to show and a letter that says exactly what you delivered.

And you leave with

  • Verified certificate

    Issued by Ezitech Institute and verifiable by employers.

  • Experience letter

    On company letterhead, stating exactly what you delivered.

  • A trained network

    Model weights, a training report and a served inference endpoint.

  • Interview preparation

    CV rewrite, portfolio case study and a technical mock interview.

  • A route into the team

    Top performers are considered first for paid junior roles.

  • Confidence in review

    Twelve weeks of defending your own work to a senior practitioner.

Built for you if

  • You are an undergraduate or fresh graduate in CS, IT, maths or engineering
  • You are comfortable with Python and school-level maths
  • You want to understand the training loop, not just call fit()
  • You are aiming at AI or research-adjacent roles

Not a fit if

  • You want a certificate without doing the work
  • You cannot give 25 hours a week for 12 weeks
  • You are looking for a purely theoretical course
  • You want to skip review and work alone
FAQ

Questions people ask

Everything applicants email us about, answered before you apply.

Still not sure?

Talk to the admissions team before you commit twelve weeks.

Contact Ezitech
Do I need machine learning experience before deep learning?
It helps but it is not required. Weeks 1 to 3 cover the fundamentals, and the track is deliberately built around transfer learning so you get real results early instead of waiting six weeks for a from-scratch model to converge.
How many hours a week does it take?
A minimum of 25 hours across 12 weeks, split between onsite sessions, mentor reviews and independent work. Interns who treat it as full-time finish with noticeably stronger portfolios.
Is it remote or onsite?
Hybrid. Onsite days run at the Ezitech office in Rawalpindi; the rest of the week is remote with scheduled check-ins. Candidates outside Rawalpindi and Islamabad can complete the track fully remotely if they attend the weekly review calls.
Do I need my own GPU?
No. Training runs on Google Colab and on the Ezitech GPU workstation during onsite days. The track is scoped so that every experiment fits inside those limits.
Can final-year students apply?
Yes, and many do. The capstone is regularly used as a final-year project because it is built from a real brief with real requirements. Agree the scope with your mentor in week one if your university has a set submission format.
How do I apply?
Register on the Ezitech intern portal, choose the Deep Learning track, and complete the three-step form with your education details and CV. Shortlisted candidates get a short screening call before the intake starts.
Other tracks

Not sure Deep Learning is the one?

Every AI internship runs three months, hybrid, with the same mentor model. Or take the six-month All-in-One flagship.

ML

Machine Learning

Train, evaluate and deploy models on real, messy data.

View track
Vision

Computer Vision

Detection, tracking and OCR running on real video, not sample clips.

View track
Analytics

Data Science

From messy data to a decision someone actually makes.

View track
Data engineering

Big Data

Pipelines, warehouses and datasets too large for a laptop.

View track
Applications open

Train a network you can actually explain

Three months, one shipped piece of real work, a mentor who reviews you every week. Registration takes five minutes.

Apply for this internship Talk to us first
Ezitech Institute · Rawalpindi, Pakistan · info@ezitech.org · +92 345 5555396