Internship Curriculum

6-Month Deep Dive

Specialization and scale. Go from foundational ML to building autonomous agents and production-grade systems.

Months 1-2

Engineering Foundations & Backend

Advanced Engineering Stack

Before modeling, you need to master the environment.

  • Software Engineering: Design Patterns, Clean Code, Git Workflows.
  • FastAPI Mastery: Asynchronous endpoints, Pydantic validation, Dependency Injection.
  • Containerization: Docker, Docker Compose, Multi-stage builds.
  • Database Design: SQL (PostgreSQL) vs NoSQL (MongoDB/Vector DBs).
Project 1: Scalable Data API

Build a high-performance REST API that ingests real-time data, processes it asynchronously using Celery/Redis, and stores it. Full Docker deployment required.

Months 3-4

Deep Learning & Computer Vision

Neural Architectures

Understanding the backbone of modern AI.

  • PyTorch Deep Dive: Custom Datasets, Training Loops, Auto-grad.
  • Computer Vision: CNNs, Transfer Learning, Object Detection (YOLOv8).
  • Image Segmentation: U-Net architecture, Medical Image Analysis.
Project 2: Real-time Object Detection Service

Create a video analytics pipeline that detects PPE (Personal Protective Equipment) on construction workers in real-time. Serve the model using FastAPI and WebSockets.

Months 5-6

Generative AI & Agentic Workflows

LLMs & Agents

Building systems that think and act.

  • Advanced Prompt Engineering: Chain-of-Thought, Tree-of-Thought, ReAct.
  • LangChain & LangGraph: Building stateful agents, Cyclic graphs, Human-in-the-loop.
  • Multi-Agent Systems: Orchestrating multiple agents to solve complex tasks.
  • Vector Search: Hybrid search (Sparse + Dense) with Pinecone/Weaviate.
🏆 Capstone: "Autonomous Customer Support Agent"

Build a production-grade Customer Support Chatbot using LangGraph and FastAPI.

  • Features: Intent classification, RAG for policy lookup, Action execution (processing refunds via API).
  • Architecture: Stateful graph with memory, handling interruptions and confirmations.
  • Deployment: Deployed on Cloud (AWS/Azure) with tracing via LangSmith/Arize.
Bonus

Interview Preparation Kit (50 Questions)

Comprehensive prep for Engineering and Data Science roles.

🐍 Python & System Design (10)

  1. Explain the GIL and how to achieve true parallelism in Python.
  2. How does Python's garbage collection work (Reference counting vs Generational)?
  3. Design a URL shortening service (System Design).
  4. How would you handle 1TB of data in a Pandas workflow? (Dask/Spark/Chunking).
  5. What is the difference between concurrency and parallelism?
  6. Explain Dependency Injection in FastAPI.
  7. How do REST and GraphQL differ? When to use which?
  8. What is a Decorator? Implementation of a retry decorator.
  9. Explain Asynchronous programming (async/await) in Python.
  10. How do you secure an API? (JWT, OAuth2, Rate Limiting).

🤖 Machine Learning & MLOps (20)

  1. What is Model Drift (Data vs Concept drift)? How do you detect it?
  2. Explain the difference between Batch Serving and Online Serving.
  3. How does XGBoost differ from Random Forest under the hood?
  4. Explain the trade-off between Precision and Recall.
  5. What is A/B testing in the context of ML models?
  6. How do you handle missing data in production pipelines?
  7. Explain Quantization and Pruning for model optimization.
  8. What is Shadow Deployment?
  9. How does Docker help in ML reproducibility?
  10. Explain the architecture of a Transformer model.
  11. What is the attention mechanism mathematically?
  12. Difference between L1 and L2 regularization.
  13. How do you handle imbalanced classes (SMOTE, Focal Loss)?
  14. What is a Feature Store? Why do we need it?
  15. Explain Gradient Descent and its variants (Adam, RMSprop).
  16. What is Transfer Learning?
  17. How do embeddings work?
  18. Explain ROC-AUC curve.
  19. What is K-Fold Cross Validation?
  20. How to debug an overfitting neural network?

🧠 GenAI & LLMs (20)

  1. What is RAG (Retrieval Augmented Generation)?
  2. Explain the difference between Encoder-only (BERT) and Decoder-only (GPT) models.
  3. What is the Context Window? How do we handle long contexts?
  4. Explain Temperature and Top-P sampling.
  5. What is LangChain? How does it work?
  6. What are Hallucinations? How do we reduce them?
  7. Explain ReAct prompting technique.
  8. What is Fine-tuning (LoRA/QLoRA)?
  9. How do Vector Databases work? (HNSW, Cosine Similarity).
  10. What is LangGraph? Difference from standard chains.
  11. How do you evaluate an LLM application? (RAGAS).
  12. What are Agents? How do they differ from Chains?
  13. Explain Tokenization. Byte-Pair Encoding (BPE).
  14. What is Grounding in LLMs?
  15. How to handle PII (Personally Identifiable Information) in prompts?
  16. What is Function Calling in OpenAI models?
  17. Explain the Vanishing Gradient problem.
  18. What is Zero-shot vs Few-shot learning?
  19. Challenges of deploying LLMs in production (Latency, Cost).
  20. Future of LLMs: What is Mixture of Experts (MoE)?
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