CV -- Sam Rivera【免费下载链接】career-opsOpen-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)项目地址: https://gitcode.com/GitHub_Trending/ca/career-opsLocation:Berlin, Germany (remote-friendly, EU timezones)Email:samexample.comLinkedIn:linkedin.com/in/sam-rivera-examplePortfolio:sam-rivera.example.devGitHub:github.com/sam-rivera-exampleProfessional SummaryTwo jobs at once for the last four years: senior AI engineer at a knowledge-graph SaaS (60K LOC of the AI subsystem, agent infra handling 4M events/month), and lead instructor at an applied AI bootcamp (5,200 teaching hours, 80 alumni placed into ML/AI roles, 92% completion across 11 cohorts). I keep doing both because the bootcamp work forces me to write code people can actually read, and the production work keeps the curriculum from becoming a fairy tale. I will take a role that is mostly engineering, mostly teaching, or both.Recent engineering (last 12 months)Wrote a planner / executor / critic loop on top of LangGraph for the internal agent platform. HITL checkpoints, Redis-backed checkpointer, ~14K LOC TypeScript on Bun. Average task-completion latency went from 9.2s to 5.7s after the executor stopped re-running the planner on retries (this was the bug that cost me a weekend).agent-skills-kit(fictional example) is the open source spinoff of the same loop. 2.4K stars, 11 outside contributors. I mostly maintain it on Sundays.47 merged PRs across two production codebases in the last year. Happy to walk through any of them in an interview, including the ones I would write differently now.Work ExperienceKnowledge-Graph SaaS GmbH -- BerlinSenior AI Engineer / Team Lead2022-2026Owned the AI subsystem of a Neo4j-backed enterprise knowledge graph product. The AI layer was 97K LOC at handover; I wrote or rewrote around 60K of that.Embedding pipeline: chunker, dedupe, Azure OpenAI embed, Chroma Neo4j sync. Throughput was ~50 docs/min when I picked it up. After I rewrote it around batched async and a real connection pool, it sat at 1,800 docs/min in production. The bottleneck the whole time was a single sync HTTP client nobody had thought to look at.Built the agent layer with LangChain and LangGraph using the 12-factor agents pattern. Redis-backed checkpointer, observability through LangSmith. 4M agent events/month at peak.Ran a team of three engineers and one designer. Weekly architecture review, pair programming on Tuesdays, an on-call rotation that went from we dont have one to a 30-minute response SLO with a written playbook.Built the customer-facing eval dashboard: latency, cost-per-query, hallucination rate, retrieval precision and recall. We walked through it with customers in their monthly business reviews.Applied AI Bootcamp -- Berlin (parallel role, same window)Lead Instructor, AI Engineering Track2022-2026Wrote and ran a 4-week AI Engineering curriculum: Bun, TypeScript, LangChain, LangGraph, Redis, Neo4j, LangSmith, MCP, Anthropic SDK. Six cohorts, average size 14.Wrote and ran a 4-week Applied Python for AI curriculum: Python, FastAPI, ChromaDB, Gradio, HuggingFace Transformers, wandb. Five cohorts.5,200 teaching hours total across both tracks (lecture lab 1:1 office hours). I keep a real spreadsheet, not a vibes count.80 alumni placed into AI/ML engineering roles so far. I personally maintain the outcomes tracker because the school does not.NPS 71. Completion rate 92%, against an industry baseline of 60-75% for intensive bootcamps.Wrote the assessment rubric and the capstone-week format the school later adopted across all technical tracks. I lost an argument about timeboxing the capstone and was right two cohorts later.Mid-stage AI Consultancy -- RemoteML Engineer2019-2022Nine client engagements: NLP classification, two recommender systems, and two early LLM prototypes back when GPT-3 was the only game in town.Wrote the internal eval harness the rest of the consultancy adopted. Cut vibes-check review cycles down to a 20-minute structured CI run.Mentored four junior engineers. Two are now senior ICs at FAANG-tier companies. This is the job where I figured out I liked teaching as much as building.Mobile Games Studio -- RemoteBackend Engineer2017-2019Backend services for a live-ops mobile game: matchmaking, leaderboards, IAP reconciliation. Python, Postgres, Redis.Rewrote the internal SDK onboarding doc. New-hire ramp-up went from three weeks to eight days. The old doc had a 14-step setup that nobody on the current team had actually run end-to-end.Projectsagent-skills-kit(fictional example, open source) -- TypeScript scaffolding for agent skills with HITL approval gates. 2.4K GitHub stars, 11 outside contributors, ~120 weekly active developers. Newsletter mentions in TLDR AI and Bens Bites.pplx-embed-local-runner(fictional example, open source) -- a small local runner for open embedding models with a drop-in OpenAI-compatible API. 610 stars. Three of the bootcamp lab exercises run against it instead of paid APIs.AI Engineering 4-Week Intensive -- the full curriculum: syllabus, 38 lecture scripts, 16 graded projects on Bronze / Silver / Gold / Diamond difficulty tiers. Used in six cohorts. Written by me, debugged by the students.EducationBSc Computer Science, TU Example (2017)Self-directed: Andrew Ng MLOps specialization, fast.ai Part 12, hand-rolled implementations of attention RAG from scratch.Speaking and writingWhy your bootcamps LLM module is wrong -- BerlinML meetup, 2025. ~120 in the room.Production agents, the boring parts -- internal talk at two partner companies. The boring parts are timeouts and idempotency.About 12 long-form blog posts on agent architecture, eval design, and teaching technical material to working engineers. The eval-design one is the only one Id link unprompted.SkillsEngineering: TypeScript, Python (daily), Go (I can read it and write small things), SQL. LangChain, LangGraph, Anthropic SDK, HuggingFace Transformers/Trainer, scikit-learn, PyTorch (basics, not research-level), MCP. Bun, Node.js, FastAPI, Gradio, Redis, Neo4j, ChromaDB, Postgres, Docker, GitHub Actions. LangSmith, Grafana, custom eval dashboards, wandb.Teaching: curriculum design with Bronze/Silver/Gold/Diamond difficulty tiering, capstone formats, lab vs lecture split. Delivery: lecture, hands-on lab, 1:1 office hours, code review at scale, pair programming. I have taught working engineers (career-changers), university CS students, and internal teams. Assessment work: rubrics, capstone projects, portfolio review, mock-interview design.Other: public speaking in German and English, technical writing, hiring loop design, mentorship.### 2.1 头部信息与 profile.yml 保持一致的五个字段 简历头部的 Location / Email / LinkedIn / Portfolio / GitHub 五个字段与 [profile.yml](https://link.gitcode.com/i/70c6abf0e5f2aa52717738fe1cbb9998) 的 candidate: 块一一对应location、email、linkedin、portfolio_url、github。career-ops 的唯一事实来源规则见 [modes/_shared.md](https://link.gitcode.com/i/182e82210928d89a33a7cfde037002f0) 的 Sources of Truth 表要求 cv.md 与 config/profile.yml 始终是用户面内容简历、求职信、表单回答、招聘者触达的仅有的两个来源两者不一致会触发 node cv-sync-check.mjs 的告警。双轨候选人在维护时要特别注意两侧身份字段完全相同冲突只会出现在叙事层不会出现在联系信息层。 ### 2.2 Professional Summary第一句就报出组合本身 这是双轨写法最关键的一段。README 给出的原则是**让组合本身领衔摘要**——senior AI engineer who runs the curriculum这类句式。单独任一边都不稀缺稀缺的是组合。范本的第一句话做到了三件事 - **同时报出两条轨道及其量级**工程侧 60K LOC 的 AI 子系统 月处理 4M 事件的 agent 基础设施教学侧 5,200 教学小时、80 名学员被安置到 ML/AI 岗位、11 期学员 92% 完课率 - **给出为什么同时做的因果逻辑**bootcamp 的工作逼着他写出别人真能读懂的代码生产环境的工作又让课程不至于变成童话——这比我热爱教学的口号更能通过招聘者的心智模型检验 - **明确接受范围**mostly engineering、mostly teaching、or both 都可以接。这句直接回应了双轨候选人最容易被问到的你到底想要什么。 ### 2.3 Recent engineering (last 12 months)针对欠资资格质疑的免疫段 这段不是标准简历模板的一部分而是双轨模式特有的补丁段。README 的 failure mode 表指出双轨候选人在纯工程岗位常被判underqualified你已经两年没做全职 IC 了。缓解手段就是在 CV 里加一个最近工程段只列**最近 12 个月的交付证据**。范本这段的写法有三点值得学 1. **量化到具体数字**planner/executor/critic 循环、~14K LOC TypeScript on Bun、任务完成延迟从 9.2s 降到 5.7s——并且交代了瓶颈成因executor 在重试时重复运行 planner 2. **开源成果带维护人视角**2.4K stars、11 位外部贡献者、mostly maintain it on Sundays——诚实交代投入程度反而增强可信度 3. **主动开放面试深潜**47 个已合并 PR包括我现在会写得不一样的那些——直接邀请面试官抽查把欠资风险转成可验证信号。 ### 2.4 Work Experience分层结构layered而非分区结构sectioned README 给出两种组织方式 - **分层layered推荐**一个 Professional Summary 在第一句同时点名两条轨道之后每段经历内部混排工程与教学 bullets。适用于两份工作在**同一段时期内实际并行**的情况——范本采用的就是这种Knowledge-Graph SaaS 与 Bootcamp 标注了 parallel role, same window2022-2026 同一时间窗 - **分区sectioned**单独设 Engineering Experience 与 Teaching Experience 两个大标题。适用于两条轨道发生在**不同雇主**、不需要讲成一个故事的情况。 范本 Work Experience 的 bullet 写法有几个双轨特例 - 工程段里的每一条都是接手时的状态 → 重构动作 → 生产数字三段式嵌入管道从 ~50 docs/min 提到 1,800 docs/min瓶颈被精确定位为一个没人想到要看同步 HTTP client - 教学段的每条数字都带**来源可信度声明**I keep a real spreadsheet, not a vibes count教学小时数I personally maintain the outcomes tracker because the school does not学员安置数——这是在预防面试官对教学数字的默认怀疑 - 教学段还包含**制度性贡献**评估 rubric 与 capstone-week 格式被学校推广到全部技术轨道——这类我的做法成了组织标准的证据对 Curriculum Lead 类岗位尤其有效。 ### 2.5 Projects / Skills按轨道打标签 Projects 段里三个项目分别落在 engineeringagent-skills-kit、hybridpplx-embed-local-runner三个 bootcamp 实验课对着它跑而不是付费 API、teachingAI Engineering 4-Week Intensive 课程本体——这个三分类与 [profile.yml](https://link.gitcode.com/i/70c6abf0e5f2aa52717738fe1cbb9998) 里 proof_points 的 track: 字段engineering / teaching / hybrid严格对齐。 Skills 段则直接拆成 Engineering / Teaching / Other 三块。Teaching 块列的是**教学法能力**难度分层课程设计、capstone 格式、讲授/实验配比、大规模 code review、mock 面试设计而不是会用 PPT这是区分真教学轨道与副业兴趣的关键——README 明确说 I mentor on weekends 这种量级不构成教学职业。 ## 三、配套的 profile.yml双 primary 原型与三套薪资区间 [examples/dual-track-engineer-instructor/profile.yml](https://link.gitcode.com/i/70c6abf0e5f2aa52717738fe1cbb9998) 是该目录的配置文件范本基于 canonical schema [config/profile.example.yml](https://link.gitcode.com/i/0b9ac69fb64fc6de1574fbb5ba225d1b) 扩展。核心差异集中在三处。 ### 3.1 target_roles.archetypes两个 fit: primary yaml target_roles: primary: - Senior AI Engineer - Staff AI Engineer - Senior Technical Instructor (AI/ML) - Lead Curriculum Engineer archetypes: - name: Senior AI Engineer level: Senior/Staff fit: primary track: engineering sell_when: JD emphasizes shipping production AI, agent infra, LangChain/LangGraph, eval systems, ownership of an AI subsystem - name: Senior Technical Instructor (AI/ML) level: Senior/Lead fit: primary track: teaching sell_when: JD emphasizes curriculum, cohort delivery, bootcamp/university lectureship, DevRel education, internal enablement, instructor-led training - name: AI DevRel Engineer level: Senior fit: secondary track: hybrid sell_when: JD wants someone who can ship demos, write docs, AND speak at events / run workshops -- the natural dual-track landing zone - name: Head of AI Education / Curriculum Lead level: Lead/Manager fit: secondary track: teaching sell_when: JD wants ownership of an entire curriculum, hiring managing instructors, working with industry partners - name: Forward Deployed AI Engineer level: Senior fit: adjacent track: engineering sell_when: JD wants client-facing technical work, fast prototyping, customer enablement -- teaching skills become a differentiator here对照 config/profile.example.yml 可以看到单轨示例里fit只有一条primary注释写明 primary dream role, secondary good fit, adjacent stretch。双轨示例打破了这一惯例两条primary外加secondaryDevRel、Curriculum Lead与adjacentForward Deployed作为落点缓冲。每个条目额外带两个非标准字段track:engineering / teaching / hybrid——用于把原型映射到薪资区间sell_when:——一句话描述JD 出现哪些信号时主打这条轨道等于给评估器一份可读的匹配规则。从 modes/_profile.template.md 的结构看用户侧的modes/_profile.md用 Your Target Roles 表与 Your Adaptive Framing 表分别声明原型与某类岗位出现时该强调自己的哪一面、证据来自哪个文件。双轨候选人在这两张表里为每条轨道各占一行框架逻辑对全部 primary 原型施以同等严格度——这正是 README 说的 The skill applies equal rigor to all primary archetypes。3.2compensation默认区间 alternate_rangescompensation: # Default range engineering range (typically higher). target_range: EUR 95K-130K currency: EUR minimum: EUR 80K location_flexibility: Remote within EU; up to 1 week/month on-site in Berlin or any EU city # alternate_ranges -- non-standard but the dual-track pattern needs it. # The evaluator should pick the range matching the detected archetype track. alternate_ranges: - track: teaching target_range: EUR 70K-95K minimum: EUR 60K note: Teaching roles pay less than engineering. Walk-away is lower because the work is intrinsically rewarding, but not unlimited. - track: hybrid target_range: EUR 90K-120K minimum: EUR 75K note: DevRel and Forward-Deployed roles -- price between the two tracks.target_range/minimum/currency/location_flexibility这四个键与 config/profile.example.yml 的compensation块完全同构alternate_ranges是双轨示例引入的扩展块。设计逻辑是默认区间放工程侧通常是更高的一档教学侧与混合侧放进alternate_ranges每条带track标签和一段note说明走人线walk-away为何不同。注意范本里教学区间的minimum明显下移——这是有意的README 提示如果你某一轨道的薪资预期不可让步就选那条轨道做单轨另一条只当求职信里的差异化点。3.3narrativeheadline、exit_story、带 track 标签的 proof_pointsnarrative: headline: Senior AI engineer who also runs the curriculum -- 60K LOC in production, 5,200 teaching hours, 80 careers launched exit_story: Spent 4 years running an AI subsystem at a knowledge-graph SaaS in parallel with leading the AI Engineering track at a Berlin bootcamp. Both missions are complete -- now optimizing for a single role that uses both sides, or for one side at full intensity if the team is right. superpowers: - Ship production AI systems end-to-end (LangChain, LangGraph, Neo4j, Redis) - Design curriculum that produces hireable engineers (92% completion, 80 placed) - Explain hard technical material in plain language -- in two languages - Run incident reviews and code reviews at scale without burning out the team - Bridge engineering and education -- internal docs that people actually read proof_points: - name: Knowledge-Graph SaaS AI subsystem url: https://sam-rivera.example.dev/case-study-knowledge-graph hero_metric: 60K LOC owned, 4M agent events/month, 38% latency cut track: engineering - name: agent-skills-kit url: https://github.com/sam-rivera-example/agent-skills-kit hero_metric: 2,400 GitHub stars, 11 contributors track: engineering - name: AI Engineering 4-Week Intensive (curriculum) url: https://sam-rivera.example.dev/curriculum-ai-eng hero_metric: 6 cohorts, NPS 71, 92% completion, 80 careers launched track: teaching - name: pplx-embed-local-runner url: https://github.com/sam-rivera-example/pplx-embed-local-runner hero_metric: 610 stars, used in 3 lab exercises track: hybrid【免费下载链接】career-opsOpen-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)项目地址: https://gitcode.com/GitHub_Trending/ca/career-ops创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
CV -- Sam Rivera【免费下载链接】career-opsOpen-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)项目地址: https://gitcode.com/GitHub_Trending/ca/career-opsLocation:Berlin, Germany (remote-friendly, EU timezones)Email:samexample.comLinkedIn:linkedin.com/in/sam-rivera-examplePortfolio:sam-rivera.example.devGitHub:github.com/sam-rivera-exampleProfessional SummaryTwo jobs at once for the last four years: senior AI engineer at a knowledge-graph SaaS (60K LOC of the AI subsystem, agent infra handling 4M events/month), and lead instructor at an applied AI bootcamp (5,200 teaching hours, 80 alumni placed into ML/AI roles, 92% completion across 11 cohorts). I keep doing both because the bootcamp work forces me to write code people can actually read, and the production work keeps the curriculum from becoming a fairy tale. I will take a role that is mostly engineering, mostly teaching, or both.Recent engineering (last 12 months)Wrote a planner / executor / critic loop on top of LangGraph for the internal agent platform. HITL checkpoints, Redis-backed checkpointer, ~14K LOC TypeScript on Bun. Average task-completion latency went from 9.2s to 5.7s after the executor stopped re-running the planner on retries (this was the bug that cost me a weekend).agent-skills-kit(fictional example) is the open source spinoff of the same loop. 2.4K stars, 11 outside contributors. I mostly maintain it on Sundays.47 merged PRs across two production codebases in the last year. Happy to walk through any of them in an interview, including the ones I would write differently now.Work ExperienceKnowledge-Graph SaaS GmbH -- BerlinSenior AI Engineer / Team Lead2022-2026Owned the AI subsystem of a Neo4j-backed enterprise knowledge graph product. The AI layer was 97K LOC at handover; I wrote or rewrote around 60K of that.Embedding pipeline: chunker, dedupe, Azure OpenAI embed, Chroma Neo4j sync. Throughput was ~50 docs/min when I picked it up. After I rewrote it around batched async and a real connection pool, it sat at 1,800 docs/min in production. The bottleneck the whole time was a single sync HTTP client nobody had thought to look at.Built the agent layer with LangChain and LangGraph using the 12-factor agents pattern. Redis-backed checkpointer, observability through LangSmith. 4M agent events/month at peak.Ran a team of three engineers and one designer. Weekly architecture review, pair programming on Tuesdays, an on-call rotation that went from we dont have one to a 30-minute response SLO with a written playbook.Built the customer-facing eval dashboard: latency, cost-per-query, hallucination rate, retrieval precision and recall. We walked through it with customers in their monthly business reviews.Applied AI Bootcamp -- Berlin (parallel role, same window)Lead Instructor, AI Engineering Track2022-2026Wrote and ran a 4-week AI Engineering curriculum: Bun, TypeScript, LangChain, LangGraph, Redis, Neo4j, LangSmith, MCP, Anthropic SDK. Six cohorts, average size 14.Wrote and ran a 4-week Applied Python for AI curriculum: Python, FastAPI, ChromaDB, Gradio, HuggingFace Transformers, wandb. Five cohorts.5,200 teaching hours total across both tracks (lecture lab 1:1 office hours). I keep a real spreadsheet, not a vibes count.80 alumni placed into AI/ML engineering roles so far. I personally maintain the outcomes tracker because the school does not.NPS 71. Completion rate 92%, against an industry baseline of 60-75% for intensive bootcamps.Wrote the assessment rubric and the capstone-week format the school later adopted across all technical tracks. I lost an argument about timeboxing the capstone and was right two cohorts later.Mid-stage AI Consultancy -- RemoteML Engineer2019-2022Nine client engagements: NLP classification, two recommender systems, and two early LLM prototypes back when GPT-3 was the only game in town.Wrote the internal eval harness the rest of the consultancy adopted. Cut vibes-check review cycles down to a 20-minute structured CI run.Mentored four junior engineers. Two are now senior ICs at FAANG-tier companies. This is the job where I figured out I liked teaching as much as building.Mobile Games Studio -- RemoteBackend Engineer2017-2019Backend services for a live-ops mobile game: matchmaking, leaderboards, IAP reconciliation. Python, Postgres, Redis.Rewrote the internal SDK onboarding doc. New-hire ramp-up went from three weeks to eight days. The old doc had a 14-step setup that nobody on the current team had actually run end-to-end.Projectsagent-skills-kit(fictional example, open source) -- TypeScript scaffolding for agent skills with HITL approval gates. 2.4K GitHub stars, 11 outside contributors, ~120 weekly active developers. Newsletter mentions in TLDR AI and Bens Bites.pplx-embed-local-runner(fictional example, open source) -- a small local runner for open embedding models with a drop-in OpenAI-compatible API. 610 stars. Three of the bootcamp lab exercises run against it instead of paid APIs.AI Engineering 4-Week Intensive -- the full curriculum: syllabus, 38 lecture scripts, 16 graded projects on Bronze / Silver / Gold / Diamond difficulty tiers. Used in six cohorts. Written by me, debugged by the students.EducationBSc Computer Science, TU Example (2017)Self-directed: Andrew Ng MLOps specialization, fast.ai Part 12, hand-rolled implementations of attention RAG from scratch.Speaking and writingWhy your bootcamps LLM module is wrong -- BerlinML meetup, 2025. ~120 in the room.Production agents, the boring parts -- internal talk at two partner companies. The boring parts are timeouts and idempotency.About 12 long-form blog posts on agent architecture, eval design, and teaching technical material to working engineers. The eval-design one is the only one Id link unprompted.SkillsEngineering: TypeScript, Python (daily), Go (I can read it and write small things), SQL. LangChain, LangGraph, Anthropic SDK, HuggingFace Transformers/Trainer, scikit-learn, PyTorch (basics, not research-level), MCP. Bun, Node.js, FastAPI, Gradio, Redis, Neo4j, ChromaDB, Postgres, Docker, GitHub Actions. LangSmith, Grafana, custom eval dashboards, wandb.Teaching: curriculum design with Bronze/Silver/Gold/Diamond difficulty tiering, capstone formats, lab vs lecture split. Delivery: lecture, hands-on lab, 1:1 office hours, code review at scale, pair programming. I have taught working engineers (career-changers), university CS students, and internal teams. Assessment work: rubrics, capstone projects, portfolio review, mock-interview design.Other: public speaking in German and English, technical writing, hiring loop design, mentorship.### 2.1 头部信息与 profile.yml 保持一致的五个字段 简历头部的 Location / Email / LinkedIn / Portfolio / GitHub 五个字段与 [profile.yml](https://link.gitcode.com/i/70c6abf0e5f2aa52717738fe1cbb9998) 的 candidate: 块一一对应location、email、linkedin、portfolio_url、github。career-ops 的唯一事实来源规则见 [modes/_shared.md](https://link.gitcode.com/i/182e82210928d89a33a7cfde037002f0) 的 Sources of Truth 表要求 cv.md 与 config/profile.yml 始终是用户面内容简历、求职信、表单回答、招聘者触达的仅有的两个来源两者不一致会触发 node cv-sync-check.mjs 的告警。双轨候选人在维护时要特别注意两侧身份字段完全相同冲突只会出现在叙事层不会出现在联系信息层。 ### 2.2 Professional Summary第一句就报出组合本身 这是双轨写法最关键的一段。README 给出的原则是**让组合本身领衔摘要**——senior AI engineer who runs the curriculum这类句式。单独任一边都不稀缺稀缺的是组合。范本的第一句话做到了三件事 - **同时报出两条轨道及其量级**工程侧 60K LOC 的 AI 子系统 月处理 4M 事件的 agent 基础设施教学侧 5,200 教学小时、80 名学员被安置到 ML/AI 岗位、11 期学员 92% 完课率 - **给出为什么同时做的因果逻辑**bootcamp 的工作逼着他写出别人真能读懂的代码生产环境的工作又让课程不至于变成童话——这比我热爱教学的口号更能通过招聘者的心智模型检验 - **明确接受范围**mostly engineering、mostly teaching、or both 都可以接。这句直接回应了双轨候选人最容易被问到的你到底想要什么。 ### 2.3 Recent engineering (last 12 months)针对欠资资格质疑的免疫段 这段不是标准简历模板的一部分而是双轨模式特有的补丁段。README 的 failure mode 表指出双轨候选人在纯工程岗位常被判underqualified你已经两年没做全职 IC 了。缓解手段就是在 CV 里加一个最近工程段只列**最近 12 个月的交付证据**。范本这段的写法有三点值得学 1. **量化到具体数字**planner/executor/critic 循环、~14K LOC TypeScript on Bun、任务完成延迟从 9.2s 降到 5.7s——并且交代了瓶颈成因executor 在重试时重复运行 planner 2. **开源成果带维护人视角**2.4K stars、11 位外部贡献者、mostly maintain it on Sundays——诚实交代投入程度反而增强可信度 3. **主动开放面试深潜**47 个已合并 PR包括我现在会写得不一样的那些——直接邀请面试官抽查把欠资风险转成可验证信号。 ### 2.4 Work Experience分层结构layered而非分区结构sectioned README 给出两种组织方式 - **分层layered推荐**一个 Professional Summary 在第一句同时点名两条轨道之后每段经历内部混排工程与教学 bullets。适用于两份工作在**同一段时期内实际并行**的情况——范本采用的就是这种Knowledge-Graph SaaS 与 Bootcamp 标注了 parallel role, same window2022-2026 同一时间窗 - **分区sectioned**单独设 Engineering Experience 与 Teaching Experience 两个大标题。适用于两条轨道发生在**不同雇主**、不需要讲成一个故事的情况。 范本 Work Experience 的 bullet 写法有几个双轨特例 - 工程段里的每一条都是接手时的状态 → 重构动作 → 生产数字三段式嵌入管道从 ~50 docs/min 提到 1,800 docs/min瓶颈被精确定位为一个没人想到要看同步 HTTP client - 教学段的每条数字都带**来源可信度声明**I keep a real spreadsheet, not a vibes count教学小时数I personally maintain the outcomes tracker because the school does not学员安置数——这是在预防面试官对教学数字的默认怀疑 - 教学段还包含**制度性贡献**评估 rubric 与 capstone-week 格式被学校推广到全部技术轨道——这类我的做法成了组织标准的证据对 Curriculum Lead 类岗位尤其有效。 ### 2.5 Projects / Skills按轨道打标签 Projects 段里三个项目分别落在 engineeringagent-skills-kit、hybridpplx-embed-local-runner三个 bootcamp 实验课对着它跑而不是付费 API、teachingAI Engineering 4-Week Intensive 课程本体——这个三分类与 [profile.yml](https://link.gitcode.com/i/70c6abf0e5f2aa52717738fe1cbb9998) 里 proof_points 的 track: 字段engineering / teaching / hybrid严格对齐。 Skills 段则直接拆成 Engineering / Teaching / Other 三块。Teaching 块列的是**教学法能力**难度分层课程设计、capstone 格式、讲授/实验配比、大规模 code review、mock 面试设计而不是会用 PPT这是区分真教学轨道与副业兴趣的关键——README 明确说 I mentor on weekends 这种量级不构成教学职业。 ## 三、配套的 profile.yml双 primary 原型与三套薪资区间 [examples/dual-track-engineer-instructor/profile.yml](https://link.gitcode.com/i/70c6abf0e5f2aa52717738fe1cbb9998) 是该目录的配置文件范本基于 canonical schema [config/profile.example.yml](https://link.gitcode.com/i/0b9ac69fb64fc6de1574fbb5ba225d1b) 扩展。核心差异集中在三处。 ### 3.1 target_roles.archetypes两个 fit: primary yaml target_roles: primary: - Senior AI Engineer - Staff AI Engineer - Senior Technical Instructor (AI/ML) - Lead Curriculum Engineer archetypes: - name: Senior AI Engineer level: Senior/Staff fit: primary track: engineering sell_when: JD emphasizes shipping production AI, agent infra, LangChain/LangGraph, eval systems, ownership of an AI subsystem - name: Senior Technical Instructor (AI/ML) level: Senior/Lead fit: primary track: teaching sell_when: JD emphasizes curriculum, cohort delivery, bootcamp/university lectureship, DevRel education, internal enablement, instructor-led training - name: AI DevRel Engineer level: Senior fit: secondary track: hybrid sell_when: JD wants someone who can ship demos, write docs, AND speak at events / run workshops -- the natural dual-track landing zone - name: Head of AI Education / Curriculum Lead level: Lead/Manager fit: secondary track: teaching sell_when: JD wants ownership of an entire curriculum, hiring managing instructors, working with industry partners - name: Forward Deployed AI Engineer level: Senior fit: adjacent track: engineering sell_when: JD wants client-facing technical work, fast prototyping, customer enablement -- teaching skills become a differentiator here对照 config/profile.example.yml 可以看到单轨示例里fit只有一条primary注释写明 primary dream role, secondary good fit, adjacent stretch。双轨示例打破了这一惯例两条primary外加secondaryDevRel、Curriculum Lead与adjacentForward Deployed作为落点缓冲。每个条目额外带两个非标准字段track:engineering / teaching / hybrid——用于把原型映射到薪资区间sell_when:——一句话描述JD 出现哪些信号时主打这条轨道等于给评估器一份可读的匹配规则。从 modes/_profile.template.md 的结构看用户侧的modes/_profile.md用 Your Target Roles 表与 Your Adaptive Framing 表分别声明原型与某类岗位出现时该强调自己的哪一面、证据来自哪个文件。双轨候选人在这两张表里为每条轨道各占一行框架逻辑对全部 primary 原型施以同等严格度——这正是 README 说的 The skill applies equal rigor to all primary archetypes。3.2compensation默认区间 alternate_rangescompensation: # Default range engineering range (typically higher). target_range: EUR 95K-130K currency: EUR minimum: EUR 80K location_flexibility: Remote within EU; up to 1 week/month on-site in Berlin or any EU city # alternate_ranges -- non-standard but the dual-track pattern needs it. # The evaluator should pick the range matching the detected archetype track. alternate_ranges: - track: teaching target_range: EUR 70K-95K minimum: EUR 60K note: Teaching roles pay less than engineering. Walk-away is lower because the work is intrinsically rewarding, but not unlimited. - track: hybrid target_range: EUR 90K-120K minimum: EUR 75K note: DevRel and Forward-Deployed roles -- price between the two tracks.target_range/minimum/currency/location_flexibility这四个键与 config/profile.example.yml 的compensation块完全同构alternate_ranges是双轨示例引入的扩展块。设计逻辑是默认区间放工程侧通常是更高的一档教学侧与混合侧放进alternate_ranges每条带track标签和一段note说明走人线walk-away为何不同。注意范本里教学区间的minimum明显下移——这是有意的README 提示如果你某一轨道的薪资预期不可让步就选那条轨道做单轨另一条只当求职信里的差异化点。3.3narrativeheadline、exit_story、带 track 标签的 proof_pointsnarrative: headline: Senior AI engineer who also runs the curriculum -- 60K LOC in production, 5,200 teaching hours, 80 careers launched exit_story: Spent 4 years running an AI subsystem at a knowledge-graph SaaS in parallel with leading the AI Engineering track at a Berlin bootcamp. Both missions are complete -- now optimizing for a single role that uses both sides, or for one side at full intensity if the team is right. superpowers: - Ship production AI systems end-to-end (LangChain, LangGraph, Neo4j, Redis) - Design curriculum that produces hireable engineers (92% completion, 80 placed) - Explain hard technical material in plain language -- in two languages - Run incident reviews and code reviews at scale without burning out the team - Bridge engineering and education -- internal docs that people actually read proof_points: - name: Knowledge-Graph SaaS AI subsystem url: https://sam-rivera.example.dev/case-study-knowledge-graph hero_metric: 60K LOC owned, 4M agent events/month, 38% latency cut track: engineering - name: agent-skills-kit url: https://github.com/sam-rivera-example/agent-skills-kit hero_metric: 2,400 GitHub stars, 11 contributors track: engineering - name: AI Engineering 4-Week Intensive (curriculum) url: https://sam-rivera.example.dev/curriculum-ai-eng hero_metric: 6 cohorts, NPS 71, 92% completion, 80 careers launched track: teaching - name: pplx-embed-local-runner url: https://github.com/sam-rivera-example/pplx-embed-local-runner hero_metric: 610 stars, used in 3 lab exercises track: hybrid【免费下载链接】career-opsOpen-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)项目地址: https://gitcode.com/GitHub_Trending/ca/career-ops创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
