DEPARTMENT: ARTIFICIAL INTELLIGENCE & MACHINE LEARNING // AI-MACHINE-LEARNING-ENGINEERING-PROGRAM
VIRTUAL INTERNSHIP90 Days (12 Weeks)•Department: Artificial Intelligence & Machine Learning
AI & Machine Learning Engineering Program
An intensive 90-day engineering program covering deep neural networks, computer vision, sequence modeling, and production model optimization with PyTorch and ONNX.
Tuition Fee₹499/ verified seat
Sprint Duration90 Days12 weekly phases
Lead MentorPriya NairSenior AI & Machine Learning Research Lead
MSME RECOGNIZED PROGRAMUDYAM-BR-28-0036675
Evaluated against Staff Engineer pull-request rubrics, automated CI code coverage thresholds, and immutable SHA-256 attestation.
SYLLABUS & MILESTONE DELIVERABLES
12 SPRINT PHASES // 90 Days (12 Weeks)Week-by-Week Technical Curriculum
WEEK 01GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Tensors & Autograd Foundations
Core Engineering Topics:
PyTorch tensorsGradient descentLoss functions
Required Task Deliverable:
Custom neural network from scratch with backprop
WEEK 02GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Feedforward Neural Networks
Core Engineering Topics:
Batch normalizationDropout regularizationOptimizers
Required Task Deliverable:
Trained deep classifier with learning rate scheduling
WEEK 03GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Convolutional Architectures
Core Engineering Topics:
Kernel convolutionsPooling layersResNet blocks
Required Task Deliverable:
Image classification model with >90% validation accuracy
WEEK 04GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Computer Vision Applications
Core Engineering Topics:
Transfer learningData augmentationHeatmap CAMs
Required Task Deliverable:
Fine-tuned Vision model with Grad-CAM explanations
WEEK 05GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Recurrent & Sequence Models
Core Engineering Topics:
LSTM unitsGRU gatesTime-series forecasting
Required Task Deliverable:
Sequential prediction model on financial time series
WEEK 06GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Attention Mechanisms & Transformers
Core Engineering Topics:
Self-attention mathMulti-head attentionPosition encodings
Required Task Deliverable:
Mini-transformer implementation in PyTorch
WEEK 07GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100NLP Fine-Tuning & HuggingFace
Core Engineering Topics:
HuggingFace datasetsTokenizer pipelinesLoRA adapters
Required Task Deliverable:
Domain-adapted sentiment and classification model
WEEK 08GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Generative Models & VAEs
Core Engineering Topics:
Variational autoencodersLatent spaceReconstruction loss
Required Task Deliverable:
Trained VAE with image generation samples
WEEK 09GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Model Optimization & Quantization
Core Engineering Topics:
INT8 quantizationONNX exportTensorRT inference
Required Task Deliverable:
Optimized model achieving 3x latency reduction
WEEK 010GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Production Inference Architecture
Core Engineering Topics:
FastAPI async workersBatching queuesDocker packaging
Required Task Deliverable:
Containerized model inference microservice
WEEK 011GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100MLOps & Model Monitoring
Core Engineering Topics:
Drift detectionLogging telemetryPrometheus alerts
Required Task Deliverable:
MLOps pipeline with automated data drift metrics
WEEK 012GATE_STATUS: MANDATORY EVALUATION
PR_MIN_PASSING: 75/100Capstone Defense & Evaluation
Core Engineering Topics:
End-to-end evaluationPeer code reviewsFinal grading
Required Task Deliverable:
Production AI capstone project and technical report
Competency Attestation Objectives
OUTCOME VERIFICATIONImplement deep neural networks from scratch using PyTorch autograd and custom layers
Train Convolutional Neural Networks (CNNs) for object detection and image segmentation
Fine-tune transformer sequence models for natural language processing
Perform model quantization, weight pruning, and ONNX runtime optimizations
Deploy scalable REST inference endpoints with FastAPI and Docker containers
CODE REVIEW RUBRIC
Senior Staff Review Matrix
Applied strictly to every pull request before sprint credit is granted.
Model Performance & Metrics25 pts
Model convergence, validation loss, and generalization capability
Architecture & PyTorch Idioms25 pts
Proper use of PyTorch modules, dataset loaders, and typing
Optimization & Deployment25 pts
Effective inference latency benchmarking and containerization
Documentation & Technical Defense25 pts
Clear explanations of loss curves, trade-offs, and design choices
COMPLETION CREDENTIALS
Certificate Delivery Options
Unlocked automatically upon achieving verified completion of all sprint tasks.
Option A: Verified E-Certificate₹149
Instant high-resolution PDF with verifiable cryptographic QR code and SHA-256 ledger digest.
Option B: Printed Hard Copy Certificate₹499
Premium textured parchment with holographic security seal dispatched to your India mailing address.
Ready to Begin Your Sprint?
Enroll today to receive your official offer letter and start working on Phase 01 tasks.