Internship Curriculum

1-Year AI Fellowship

The ultimate career accelerator. Master the entire data stack, lead teams, and architect next-gen AI systems.

Q1 (Months 1-3)

The Full Stack Data Scientist

Foundations & Data Engineering

Building the pipes before the model.

  • Advanced Python & Algorithms: Data Structures, Time Complexity, Asyncio.
  • Cloud Engineering (AWS): EC2, S3, Lambda, IAM, RDS.
  • Data Pipelines: ETL/ELT with Airflow, dbt for data transformation.
  • SQL Mastery: Database optimization, Indexing, Partitioning.
Project Q1: Serverless Data Lake

Architect a real-time data ingestion pipeline on AWS. Use API Gateway -> Lambda -> Kinesis -> S3. Process data with Glue and query with Athena.

Q2 (Months 4-6)

Deep Learning & Research

Advanced Modeling

Implementing papers and state-of-the-art models.

  • Computer Vision: 3D Vision, GANs, Diffusion Models (Stable Diffusion architecture).
  • NLP Research: Attention is All You Need (Paper implementation), BERT pre-training.
  • Math for AI: Linear Algebra, Calculus, Probability Theory deep dive.
Project Q2: Custom Medical Diagnostic AI

Train a U-Net model for cell segmentation from scratch. Implement a custom loss function (Dice Loss). Deploy the inference engine on a GPU instance.

Q3 (Months 7-9)

MLOps & Production Engineering

Scalable Infrastructure

Taking models out of notebooks and into the world.

  • Container Orchestration: Kubernetes (K8s) basics, Helm charts.
  • ML Platforms: Kubeflow, MLflow Registry, Feature Stores (Feast).
  • CI/CD for ML: GitHub Actions, Automated testing, Model versioning.
  • Monitoring: Drift detection, Prometheus/Grafana dashboards.
Project Q3: End-to-End MLOps Platform

Build a "Push-to-Deploy" system. Committing code triggers a pipeline: Data Validation -> Training -> Evaluation -> Canary Deployment to K8s cluster.

Q4 (Months 10-12)

GenAI Architect & Leadership

The Cutting Edge

Architecting complex, autonomous systems.

  • LLM Ops: Fine-tuning (Llama 3), RLHF basics, Evaluation (TruLens/RAGAS).
  • Agentic AI: Multi-Agent orchestration with LangGraph, Tool use, Memory management.
  • System Design: Designing scalable, fault-tolerant distributed systems.
  • Technical Leadership: Code review, Mentoring juniors, RFC writing.
🏆 Capstone: "Enterprise Knowledge Graph RAG"

Build a massive-scale RAG system that ingests company data (Confluence, Slack, Drive), builds a Knowledge Graph, and answers complex queries using a fine-tuned Llama 3 model orchestrated by LangGraph agents.

Bonus

Senior Interview Kit (50+ Questions)

Targeting Senior Engineer & Architect roles.

🏛️ System Design & Architecture (15)

  1. Design a real-time recommendation engine for TikTok (High concurrency).
  2. How would you architect a data pipeline for processing petabytes of logs?
  3. Design a scalable vector search system like Pinecone.
  4. How do you handle data consistency in distributed systems? (CAP Theorem).
  5. Design an MLOps platform for a team of 50 data scientists.
  6. How would you scale a WebSocket server to 1M concurrent connections?
  7. Explain Load Balancing strategies (L4 vs L7).
  8. How do you design for failure? (Circuit Breakers, Retries, Fallbacks).
  9. Design a Distributed Rate Limiter.
  10. How would you migrate a monolith legacy ML system to microservices?
  11. Explain Sharding vs Replication in databases.
  12. How to minimize latency in an LLM application? (Caching, Streaming, Speculative Decoding).
  13. Design a feature store.
  14. How to handle "Thundering Herd" problem?
  15. Explain Event-Driven Architecture vs Request-Response.

🧠 Advanced GenAI & Research (15)

  1. How does PPO (Proximal Policy Optimization) work in RLHF?
  2. Explain the Scaling Laws of LLMs (Chinchilla).
  3. How does GraphRAG differ from standard Vector RAG?
  4. Explain Sparse Mixture of Experts (MoE) architecture.
  5. How do you prevent prompt injection attacks? (Guardrails).
  6. Explain Contrastive Learning (CLIP).
  7. What is DPO (Direct Preference Optimization)?
  8. How does KV Caching speed up transformer inference?
  9. Explain the "Reversal Curse" in LLMs.
  10. How to evaluate hallucinations quantitatively?
  11. Explain Long-Context attention mechanisms (Ring Attention).
  12. What is Speculative Sampling?
  13. How do you fine-tune embeddings?
  14. Difference between Soft Prompting and Prefix Tuning.
  15. How do Diffusion models work mathematically? (Forward/Reverse process).

🛠️ MLOps & Engineering (20)

  1. How do you upgrade a Kubernetes cluster without downtime?
  2. Explain the difference between Docker Swarm and Kubernetes.
  3. How to secure sensitive data in an ML pipeline?
  4. What is "Training-Serving Skew"? How to fix it?
  5. Explain how GPU scheduling works in K8s.
  6. How do you optimize Docker image size for Python ML apps?
  7. Explain GitOps methodology (ArgoCD).
  8. How to handle model versioning and rollback?
  9. What is a DAG in Airflow? How to handle backfilling?
  10. How to debug a memory leak in a Python production service?
  11. Explain the concept of "Sidecar" pattern in K8s.
  12. How to optimize costs for cloud GPU inference? (Spot instances, Auto-scaling).
  13. What is ONNX? Why use it?
  14. Explain TensorRT optimization.
  15. How to profile a slow PyTorch training loop?
  16. What is Distillation? How to distill a large model to a smaller one?
  17. How to handle API versioning?
  18. What is Service Mesh (Istio)? Do we need it for MLOps?
  19. Explain Structured Logging vs Unstructured Logging.
  20. How do you effectively conduct a Post-Mortem after an incident?
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