## AI Engineering Take-Home Assignments

Part of [AI Engineering Fundamentals](/content/lls/acbce0/index.html)

Hosted by Alexey Grigorev

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752 students

In this video

00:00:00 Introduction and Webinar Series Overview  
00:04:56 Deep Dive into AI Engineer Home Assignments  
00:08:36 Setting Up the Q&A and Interactive Session  
00:11:22 Analyzing Common Home Assignment Tasks  
00:18:49 Audience Q&A on AI Engineering Roles  
00:27:25 Defining the Live-Coding Task: Blood Test Analyzer  
00:36:18 Project Scaffolding with an AI Assistant  
00:48:52 Live Code Review and Refinement  
01:00:11 Implementing and Running Application Tests  
01:18:21 Strategies for Evaluation and Data Logging  
01:23:29 Preparing for the Post-Assignment Deep Dive Interview  
01:36:42 Live Demo: Testing with a German Blood Test Report  
01:40:25 Conclusion and Final Remarks

### What you'll learn

- **Home Assignments Analysis**  
  Analyze very recent submissions and tasks from Q4 2025 and Q1 2026 to understand current AI hiring standards.

- **Implementation Discussion**  
  Break down multiple real-world assignment prompts to discuss the most effective architectural approaches and trade-offs.

- **End-to-End Document Implementation**  
  Build a complete solution for a document-instruction task, focusing on high-accuracy PDF parsing and data extraction.

### Why this topic matters

The take-home assignment is where your ability to build production-ready systems is truly measured. By analyzing data from recent hiring cycles in late 2025 and early 2026, you’ll see what top-tier companies expect. You’ll move beyond understanding what is asked to mastering how it’s done, through deep discussion and a full end-to-end implementation of a complex document processing challenge.

### You'll learn from

**Alexey Grigorev**  
Principal Data Scientist | Book Author | Instructor to 100k+ Students World-Wide  
Alexey Grigorev is the founder of [DataTalks.Club](https://datatalks.club/) and the creator of the popular Zoomcamp series. With 15 years of experience in software engineering and over 12 years in machine learning, he has built and deployed large-scale ML systems at companies like OLX Group and Simplaex.

An advocate for practical, hands-on education, Alexey has taught over 100,000 students, focusing on a code-first approach to help learners build real-world skills.

In the past, he was an active participant in data science competitions. A Kaggle Master, Alexey has achieved top rankings in several challenges, including 1st place in the NIPS'17 Criteo Challenge and 2nd place in the WSDM Cup 2017: Vandalism Detection.

He is also the author of several technical books, including the widely read Machine Learning Bookcamp.