This article discusses a live session with Alejandro Aboy, a Senior Data and AI Engineer, about the evolving role of data engineers in the context of AI. Alejandro emphasizes that strong data engineering skills, particularly data modeling and orchestration, are crucial for successful AI work and shares practical steps for integrating AI into existing data workflows.

This article introduces a comprehensive resource for learning AI engineering, focusing on building efficient and reliable intelligent systems. It offers a textbook, hands-on activities, and hardware kits, emphasizing real-world application and constraints. The goal is to train engineers who can create dependable AI systems.

DryRun Security's Contextual Security Analysis (CSA) employs a multi-pass analytical pipeline and a unique LLM-as-Judge framework to evaluate code changes for security vulnerabilities, emphasizing probabilistic reasoning over deterministic patterns. The methodology focuses on maintaining accuracy, reliability, and user satisfaction by continuously refining context and auditing findings against established criteria. This innovative approach aims to uncover non-obvious security risks that traditional tools often miss.

IBM TechXchange 2025 offers AI Engineers an opportunity to enhance their skills through hands-on coding labs, interactive sessions on advanced AI models, and workshops focused on AI governance and infrastructure. Participants can also earn certifications, engage with open-source contributors, and connect with AI experts to address technical challenges.

The article discusses the concept of an AI engineering stack, outlining the various components and tools necessary for building and deploying AI systems effectively. It emphasizes the importance of a structured approach to integrate AI into existing workflows and highlights key technologies that facilitate this process.

Paul Iusztin shares his journey into AI engineering and LLMs, highlighting the shift from traditional model fine-tuning to utilizing foundational models with a focus on prompt engineering and Retrieval-Augmented Generation (RAG). He emphasizes the importance of a structured architecture in AI applications, comprising distinct layers for infrastructure, models, and applications, as well as a feature training inference framework for efficient system design.

The author shares an experience of using Cursor, an AI coding agent, to autonomously complete a dbt project task by integrating Linear and Supabase MCP servers. Despite some limitations and the need for oversight, the author reflects on the significant advancements in software development workflows and the potential impact of these technologies on various roles within the tech industry.

The article explores the evolving nature of data and AI engineering, arguing for a shift from defined processes to empirical approaches that embrace adaptability and variability. It draws parallels between the martial arts philosophies of Bruce Lee and Chuck Norris to illustrate the need for data teams to be innovative and responsive in their work. By discussing the definitions and professional standards in engineering, the piece advocates for recognizing data and AI engineering as legitimate engineering disciplines.