How to Land an AI Engineering Job in 2026
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Lessons
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Intro to AI-Centric EvaluationAI Accelerator- Metrics and Evaluation Design - Foundation for Future Metrics Work - Building synthetic data for AI applications
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From Theory to Practice — Building Your First LLM ApplicationAI Accelerator- Understand how inference works in LLMs (prompt processing vs. autoregressive decoding) - Explore real-world AI applications: RAG, vertical models, agents, multimodal tools - Learn the five phases of the model lifecycle: pretraining to RLHF to evaluation - Compare architecture types: generic LLMs vs. ChatGPT vs. domain-specialized models - Work with tools like Hugging Face, Modal, and vector databases - Build a “Hello World” LLM inference API using OPT-125m on Modal
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Navigating the Landscape of LLM Projects & ModalitiesAI Accelerator- Compare transformer-based LLMs vs diffusion models and their use cases - Understand the "lego blocks" of LLM-based systems: prompts, embeddings, generation, inference - Explore core LLM application types: RAG, vertical models, agents, and multimodal apps - Learn how LLMs are being used in different roles and industries (e.g., healthcare, finance, legal) - Discuss practical project scoping: what to build vs outsource, how to identify viable ideas - Identify limitations of LLMs: hallucinations, lack of reasoning, sensitivity to prompts - Highlight real-world startup examples (e.g., AutoShorts, HeadshotPro) and venture-backed tools
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Bonus ContentAI Accelerator- 2 courses - [Fundamentals of transformers with Alvin Wan](https://www.newline.co/courses/fundamentals-of-transformers-live-workshop) and [Responsive LLM Applications with Server-Sent Events](https://www.newline.co/courses/responsive-llm-applications-with-server-sent-events) - Prompt engineering templates - AI newsletters, channels, X, reddit channels - Break down of LLama components - Open source models with their capabilities - Data sources - AI specific cloud services - Open source frameworks - Project ideas from other indie hackers - Bonus: FANG Machine learning interview cheatsheet - Free API Keys for building AI Applications - How people will are using GenAI in 2025 - How to stay ahead of AI Trends? - N8N and Free High-Roi AI Automation Templates worth $50,000
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Agent Design PatternsAI Accelerator- Understand agent design patterns: Tool use, Planning, Reflection, Collaboration - Learn evaluation challenges in agent systems: output variability, partial correctness - Study architecture patterns: single-agent vs constellation/multi-agent - Explore memory models, tool integration, and production constraints - Compare agent toolkits: AutoGen, LangGraph, CrewAI, and practical use cases