[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-75053":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":15,"starSnapshotCount":15,"syncStatus":28,"lastSyncTime":29,"discoverSource":30},75053,"ai-engineering-field-guide","alexeygrigorev\u002Fai-engineering-field-guide","alexeygrigorev","Research into AI engineering interview assignments, take-home challenges, and hiring practices from Q4 2025 \u002F Q1 2026",null,"HTML",4039,371,48,1,0,69,202,576,207,106.71,false,"main",true,[],"2026-06-12 04:01:17","# AI Engineering Field Guide\n\nData-driven field guide to AI engineering roles, skills, and interviews.\n\nEverything here is based on real data: 2,445 actual job descriptions, real interview experiences, and real stories from practitioners. This is not AI-generated filler dumped into a repo - every insight comes from analyzing actual data and synthesizing patterns from it.\n\nMy vision for this repo is to become **the** go-to resource for AI engineering. Like [data-science-interviews](https:\u002F\u002Fgithub.com\u002Falexeygrigorev\u002Fdata-science-interviews) but broader:\n\n- role analysis\n- job market data\n- interview questions\n- learning paths\n- and more\n\nIt's a work in progress, and I'm actively adding more content. Your input is very welcome - feedback and contributions help shape what goes in here.\n\nStar this repo to keep an eye on updates. To get notified about new content, subscribe to my newsletter: [Alexey on Data](https:\u002F\u002Falexeyondata.substack.com\u002F).\n\n\n## The AI Engineer Role\n\n- [My vision of the role](role\u002F01-my-vision.md) - how I see AI engineering, comparison with DS\u002FML\u002FDE roles, CRISP-DM for AI\n- [Skills analysis](role\u002F02-skills.md) - top skills, job types, cloud platforms, frameworks\n- [Responsibilities](role\u002F03-responsibilities.md) - patterns extracted from 5,694+ job responsibilities\n- [Use cases](role\u002F04-use-cases.md) - 4,525 real use cases showing what companies build with AI\n- [Reality vs. job postings](role\u002F05-reality-vs-postings.md) - what candidates experience vs. what's advertised\n\n\n## Interview Preparation\n\n- [Interview process](interview\u002F01-interview-process.md) - common patterns, step counts, time estimates, AI use in hiring, key takeaways\n- [Interview questions](interview\u002F02-questions.md) - consolidated from 100+ sources\n  - [Theory](interview\u002Fquestions\u002F01-theory.md) - LLMs, RAG, agents, ML fundamentals, company-specific questions\n  - [Coding](interview\u002Fquestions\u002F02-coding.md) - coding round formats, DSA problems, ML implementation exercises\n  - [Project deep dive](interview\u002Fquestions\u002F03-project-deep-dive.md) - presentation rounds, follow-up probes, what interviewers evaluate\n  - [AI system design](interview\u002Fquestions\u002F04-ai-system-design.md) - system design for AI applications\n  - [Behavioral](interview\u002Fquestions\u002F05-behavioral.md) - values, leadership, problem-solving\n  - [Home assignments](interview\u002Fquestions\u002F06-home-assignments.md) - take-home assignments and paid work trials from 100+ GitHub repos\n- [Skills that get you hired](interview\u002F03-get-hired.md) - baseline expectations, differentiators, and portfolio strategy\n- [After the interview](interview\u002F04-after-the-interview.md) - handling offers, rejections, and salary negotiation\n- [Interview trends](interview\u002F05-trends.md) - realistic assessments, AI cheating, AI-proctored rounds\n- [Company-by-company data](interview\u002Fdata\u002F) - individual interview process descriptions for 51 companies, linked to source job postings\n\n\n\n## Learning Paths\n\n- [General learning path](learning-paths\u002F) - what to learn and in what order\n- [From Data Engineer](learning-paths\u002Ffrom-data-engineer.md) - smoothest transition, 3-4 months\n- [From Data Scientist](learning-paths\u002Ffrom-data-scientist.md) - evaluation is your superpower, add engineering\n- [From ML Engineer](learning-paths\u002Ffrom-ml-engineer.md) - easiest transition, replace model call with API call\n- [From Backend Engineer](learning-paths\u002Ffrom-backend-engineer.md) - 2-3 months, add AI on top of engineering\n- [From Frontend Engineer](learning-paths\u002Ffrom-frontend-engineer.md) - backend first, then AI, unique full-stack advantage\n\n\n## Portfolio\n\n- [Project ideas](portfolio\u002F) - real project examples that demonstrate AI engineering skills\n\n\n## Job Market Data\n\n2,445 job descriptions scraped from builtin.com covering LA, NY, London, Amsterdam, Berlin, and India.\n\n- [Structured job descriptions](job-market\u002Fdata_structured\u002F) - YAML files grouped by scrape date\n- [Raw extracted postings](job-market\u002Fdata_raw\u002F) - original extracted data grouped by scrape date\n\n\n## [Awesome AI Engineering](awesome.md)\n\nCurated collection of resources we compiled while researching content for this field guide:\n\n- Practitioner interview stories\n- AI system design guides\n- Company engineering blogs\n- Books and courses\n- Case study collections\n\nSee [awesome.md](awesome.md) for the list.\n\n\n## Coming Soon\n\n- Salary analysis and compensation data\n- Community-contributed interview experiences\n\n\n## [Webinars](webinars\u002F)\n\nA 4-part event series on AI engineering careers, hosted through [Maven](https:\u002F\u002Fmaven.com\u002F) and [AI Shipping Labs](https:\u002F\u002Faishippinglabs.com\u002F):\n\n1. [A Day of an AI Engineer](webinars\u002F01-a-day-of-ai-engineer.md) - the practical reality of the role ([Maven](https:\u002F\u002Fmaven.com\u002Fp\u002Fbf6ef3\u002Fa-day-of-ai-engineer), [AI Shipping Labs](https:\u002F\u002Faishippinglabs.com\u002Fblog\u002Fwhat-is-an-ai-engineer-alexey-grigorev-perspective)) - recording available\n2. [Defining the AI Engineer Role](webinars\u002F02-defining-the-role.md) - what companies actually hire for, based on 2,400+ job descriptions ([Maven](https:\u002F\u002Fmaven.com\u002Fp\u002Ff0cada\u002Fdefining-the-ai-engineer-role)) - recording available\n3. [The Interview Process](webinars\u002F03-the-interview-process.md) - real hiring trends, technical questions, and live coding challenges ([Maven](https:\u002F\u002Fmaven.com\u002Fp\u002F69550a\u002Fai-engineering-the-interview-process)) - March 3, 2026\n4. [Take-Home Assignments](webinars\u002F04-take-home-assignments.md) - analyzing real assignments and building production-ready solutions ([Maven](https:\u002F\u002Fmaven.com\u002Fp\u002F250595\u002Fai-engineering-take-home-assignments)) - March 9, 2026\n\nHave questions? [Submit them here](https:\u002F\u002Fapp.sli.do\u002Fevent\u002FvJEZ6h5zbFRAzPfrANZxZd) - all questions will be covered during the events or afterwards.\n\n\n\n## Learn AI Engineering\n\nIf you want to learn the core skills needed for being an AI engineer, check out my course [AI Engineering Buildcamp: From RAG to Agents](https:\u002F\u002Fmaven.com\u002Falexey-grigorev\u002Ffrom-rag-to-agents) - a 9-week intensive on building production-ready AI applications.\n","AI Engineering Field Guide 是一个基于真实数据的研究项目，旨在为AI工程师的角色、技能和面试提供详尽指导。该项目通过分析2,445份实际职位描述、真实的面试经历及从业者的故事来提炼出有价值的信息，内容涵盖角色分析、市场数据、面试问题及学习路径等。其技术特点在于完全依赖于大数据分析而非人工智能生成的内容，确保了信息的真实性和实用性。适合正在寻找AI工程相关职位或希望提升自身在该领域竞争力的专业人士参考使用。",2,"2026-06-11 03:52:07","high_star"]