5 AI Engineer Projects to Build in 2026 | Ex-Google, Microsoft

Summary

In this video, AI expert Ashwarashan shares five portfolio projects that will help aspiring AI engineers stand out in 2026. These projects go beyond basic tutorials and focus on creating production-ready systems. From building a Retrieval Augmented Generation (RAG) system and a local AI assistant, to implementing fine-tuning, monitoring layers, and real-time multimodal applications, each project targets an in-demand skill. Ashwarashan’s guidance is grounded in over a decade of experience, offering practical, real-world solutions that highlight a candidate's ability to engineer robust AI systems. This video serves as a comprehensive roadmap for those looking to level up their AI careers.

Takeaways

• 😀 RAG (Retrieval-Augmented Generation) systems are highly valuable in enterprise AI and should be built with production-level considerations, such as citation enforcement and hybrid retrieval techniques.
• 😀 The gap between demo-level RAG systems and production-ready systems is significant, and mastering this gap can make a big difference when applying for jobs in AI.
• 😀 Building a local AI assistant that runs offline using small models like Llama or Mistral is an important skill, especially for scenarios requiring data privacy, cost efficiency, or limited connectivity.
• 😀 Benchmarking the performance of models in terms of latency, memory usage, and throughput is essential for demonstrating practical understanding in AI engineering.
• 😀 Fine-tuning models for specific tasks, like JSON extraction or tool-call accuracy, requires clean, labeled data and measurable improvements to demonstrate value.
• 😀 Companies care about your ability to handle performance challenges like latency and cost in real-time, multimodal systems, such as voice assistants and computer vision pipelines.
• 😀 Adding monitoring and observability layers to AI systems shows an advanced understanding of production environments, focusing on system diagnostics and issue resolution.
• 😀 The ability to evaluate model performance systematically—comparing different models and documenting trade-offs—demonstrates solid decision-making skills in AI engineering.
• 😀 Implementing continuous integration with automated quality checks ensures that your AI system remains reliable and consistent throughout development.
• 😀 Real-world AI systems are more than just functional; they require attention to resilience, graceful degradation, and handling edge cases, which is a key point that sets candidates apart in interviews.

Q & A

Why is it important to build a production-grade RAG system instead of just a demo?

Building a production-grade RAG system distinguishes you from others by showing that you understand how to scale and maintain AI systems in real-world environments. It highlights your ability to ensure trustworthiness by grounding answers in actual retrieved evidence, unlike simple demos that may generate plausible-sounding but unverified responses.

What is the primary difference between traditional BM25 search and vector-based semantic search in a RAG system?

BM25 is a keyword-based search that excels in retrieving exact terms or phrases, while vector-based semantic search understands meaning and intent. Combining both allows the system to balance precise matches and contextual understanding, enhancing the relevance of search results.

What does 'citation enforcement' mean in a RAG system, and why is it crucial?

Citation enforcement ensures that the system only generates responses supported by actual retrieved chunks. If there is no evidence for a response, the system explicitly declines to answer. This prevents hallucinations (plausible-sounding but false information) and improves trust in the system's reliability.

Why is it important to have a golden evaluation dataset in a production-grade RAG system?

A golden evaluation dataset provides manually verified question-answer pairs, which help assess the system's faithfulness and correctness. This ensures that each model update or change doesn't compromise the quality of results, a practice that's crucial for maintaining production-level AI systems.

What are the practical constraints that make running a local AI assistant offline important?

Running an AI assistant offline addresses concerns such as privacy regulations, latency, and network reliability. There are scenarios where sharing data with external services is impractical, and local inference is necessary to meet these constraints, especially for edge devices or cost-sensitive applications.

What are the key steps in benchmarking the performance of a small language model running locally?

The key steps are: 1) Rigorous benchmarking of the model's inference performance, including metrics like tokens per second and response latency. 2) Adding structure and determinism by enforcing a JSON output schema. 3) Experimenting with different temperature settings to control variability in outputs.

Why is comparing different models important in the context of running an AI assistant offline?

Comparing different models (e.g., Llama 3.2 vs. Mistral 7B) allows you to assess trade-offs in terms of memory usage, inference speed, and output quality. This enables you to choose the most suitable model for your application and demonstrate a data-driven approach to model selection.

What does adding monitoring and observability to a RAG system entail, and why is it important?

Monitoring and observability involve adding tracing to each step of the pipeline, measuring performance over time, and tracking key metrics like latency, failure rates, and cost per request. It ensures you can diagnose issues, understand system performance, and maintain reliability in production systems.

How does regression gating work in a production AI system, and what role does it play?

Regression gating involves automatically running evaluation scripts as part of the continuous integration pipeline. If quality metrics drop below a defined threshold (e.g., faithfulness), the build fails, preventing problematic updates from being deployed. This ensures consistent performance in production.

Why is fine-tuning not about making a model smarter, but about improving its performance on specific tasks?

Fine-tuning focuses on improving a model's accuracy and consistency for a specific task, especially when prompt engineering isn't enough. It's about enhancing the model's ability to handle structured tasks, like JSON extraction or function selection, where fine-tuning provides measurable improvements over general models.