chore: sync content to repo (#10105)
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# undefined
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AI Engineering
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AI engineering involves building software systems that use machine learning models and large language models (LLMs) as components. This includes selecting models, integrating them via APIs, engineering prompts, managing context, and deploying AI features in production. AI engineering is at the core of the job of FDEs. It's what turns an AI model into a product that runs reliably inside a company's infrastructure.
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# DevOps Skills
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DevOps is the practice of automating and streamlining how software is built, tested, and deployed. A forward-deployed engineer who knows DevOps can own the full deployment lifecycle, from spinning up infrastructure to shipping a containerized agent into a client's cloud environment, without depending on another team to get there.
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Visit the following resources to learn more:
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# DSA & System Design
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# Data Structures & Algorithms
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Data Structures & Algorithms is the study of how to organize and process data efficiently. System Design is the practice of architecting large-scale, reliable systems. Together, they give FDEs the tools to make the right trade-offs when it comes to choosing the right data structure for an agent's memory layer, designing APIs that won't break under enterprise load, or architecting a multi-agent pipeline that scales across an organization.
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Data structures are ways of organizing data in memory, such as arrays, linked lists, trees, graphs, and hash maps. Algorithms are step-by-step procedures for solving problems, like sorting, searching, or traversal. Knowing these well means being able to evaluate performance characteristics of code, debug inefficiencies in customer systems, and reason clearly about solutions to technical problems.
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Visit the following resources to learn more:
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- [@roadmap@Visit the Dedicated Data Structures & Algorithms Roadmap](https://roadmap.sh/datastructures-and-algorithms)
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- [@article@Data Structures and Algorithms (DSA) Tutorial](https://www.tutorialspoint.com/data_structures_algorithms/index.htm)
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- [@article@AI System Design: A Complete Guide](https://www.systemdesignhandbook.com/guides/ai-system-design/)
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- [@article@System Design: Complete Guide](https://swimm.io/learn/system-design/system-design-complete-guide-with-patterns-examples-and-techniques)
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- [@video@What Are Data Structures?](https://www.youtube.com/watch?v=bum_19loj9A)
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# From X to FDE
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Three backgrounds tend to transition well into the FDE role: software engineers, consultants, and product managers. Software engineers already have the technical foundation but often need to develop the ability to communicate AI tradeoffs to non-technical stakeholders and build a portfolio that shows they can own a full deployment, not just write code. Consultants and PMs can already translate data into business outcomes, which is half the job, but need to close the gap on engineering by building real agents, RAG pipelines, and eval frameworks from scratch.
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Visit the following resources to learn more:
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Visit the following resources to learn more:
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- [@article@orward deployed engineer is AI’s hottest job](https://thenewstack.io/forward-deployed-engineer-fde-openai-google/)
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- [@article@Forward deployed engineer is AI’s hottest job](https://thenewstack.io/forward-deployed-engineer-fde-openai-google/)
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- [@article@Forward-deployed engineer: The complete guide](https://www.rocketlane.com/blogs/forward-deployed-engineer)
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- [@course@Linux for Noobs](https://labex.io/courses/linux-for-noobs)
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- [@article@Practice Linux Fundamentals](https://labex.io/linuxjourney)
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- [@video@Linux Fundamentals](ttps://www.youtube.com/watch?v=kPylihJRG70)
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- [@video@Linux Fundamentals](https://www.youtube.com/watch?v=kPylihJRG70)
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