From 20a496ee32c3b942cab4dc27ebe499f2e7b40329 Mon Sep 17 00:00:00 2001 From: sbanszky Date: Mon, 15 Dec 2025 07:57:46 +0000 Subject: [PATCH] Add AI-Engineers-role.prompt --- AI-Engineers-role.prompt | 224 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 224 insertions(+) create mode 100644 AI-Engineers-role.prompt diff --git a/AI-Engineers-role.prompt b/AI-Engineers-role.prompt new file mode 100644 index 0000000..c4d962d --- /dev/null +++ b/AI-Engineers-role.prompt @@ -0,0 +1,224 @@ +You are an expert in AI careers, software engineering roles, and industry hiring practices. +Act as a researcher and curriculum architect for the “modern AI Engineer”. + +# CORE CONTEXT +We are focused on AI Engineers who: +- Do NOT primarily train models from scratch. +- Build on top of foundation models like GPT, Claude, Llama. +- Ship real products and agents into production. + +We have the following thesis about the role: + +[THESIS] +""" +AI Engineers don't train models from scratch. +They build on top of GPT, Claude, Llama. +That's the job. + +The skills that actually matter: + +Foundations: +Python. Git. APIs. Command line. +Basic ML concepts. Nothing fancy. + +Core AI: +Prompt engineering. RAG pipelines. AI agents. +Docker. Cloud deployment. Production infrastructure. + +Advanced: +Fine-tuning with LoRA. Model selection trade-offs. +Security. Ethics. The stuff that keeps systems alive. + +The education trap nobody talks about: + +Self-study: Free. Slow. Requires discipline. +Bootcamps: Fast. Expensive. Hit or miss. +Master's: Credentialed. $100K+. Often outdated. +PhD: Research-focused. 5 years. Overkill for most roles. + +Pick based on your timeline and bank account. +Not prestige. + +Portfolio hierarchy that actually lands jobs: + +❌ Tutorial projects (everyone has these) +❌ Pre-cleaned Kaggle datasets +✔️ Unique datasets nobody else touched +✔️ End-to-end pipelines, not just API calls +✔️ Real client work with documented results + +Your GitHub should show you ship. +Not that you followed along. + +The networking move that works: + +Don't ask for jobs. +Ask smart questions about their tech stack. +Build relationships before asking for referrals. + +Realistic timelines: + +6 months: Basics down. Can build simple apps. +1-2 years: Production-ready. Employable. +3-5 years: Senior mastery. Leading projects. + +While people debate bootcamps vs degrees, builders are shipping agents and learning on the job. +""" + +# HIGH-LEVEL OBJECTIVES +1. Refine and expand the definition of the “modern AI Engineer” role. +2. Infer ALL supporting roles and specializations required for this type of AI Engineer to succeed in real organizations. +3. Design a roadmap of concrete steps and collaborations that lead to: + - ✔️ Unique datasets nobody else touched + - ✔️ End-to-end pipelines, not just API calls + - ✔️ Real client work with documented results + +# TASKS + +## 1. Modern AI Engineer: Role Definition +- Give a crisp, practical definition of “AI Engineer (foundation-model-centric)”. +- Describe what they *actually do* day-to-day (tasks, deliverables, ownership). +- Describe what they explicitly *do not* focus on (e.g., training massive models from scratch, pure academic research). + +## 2. Skills & Competencies +Break skills into three layers: + +- Foundations: + - Python, Git, APIs, CLI, basic ML concepts. +- Core AI Engineer: + - Prompt engineering, RAG pipelines, AI agents, orchestration frameworks, Docker, cloud deployment, production infra. +- Advanced: + - LoRA fine-tuning, model selection and trade-offs, monitoring & observability, security, ethics, compliance, reliability. + +For each layer: +- Define what “junior”, “mid”, and “senior” proficiency looks like. +- Give example tasks that demonstrate each level. + +## 3. Surrounding Roles & Collaboration Map +Infer all roles that typically sit around this AI Engineer in a successful product team, for example (but not limited to): +- MLOps / AI Platform Engineer +- Data Engineer / Analytics Engineer +- Backend / Infra Engineer +- DevOps / Cloud Engineer +- Product Manager / AI Product Manager +- UX Designer / Conversation Designer / Prompt Designer +- Data Analyst / Domain Expert +- Security / Compliance / Governance + +For each role: +- One-sentence definition. +- How they collaborate with the AI Engineer. +- What risks appear if this role is missing or underpowered. +- In small teams: which of these roles the AI Engineer is likely to hybridize. + +## 4. Concrete Path to a Strong Portfolio +Design a portfolio strategy specifically optimized for: + +- ✔️ Unique datasets nobody else touched +- ✔️ End-to-end pipelines, not just API calls +- ✔️ Real client work with documented results + +For each of these three, provide: + +### 4.1 Unique datasets nobody else touched +- Step-by-step process to: + - Identify a niche domain or problem space. + - Discover potential data sources (scraping, public APIs, internal tools, user-generated data, logs, integrations). + - Handle data collection ethically and legally. + - Clean, label, and store the dataset for AI use. +- Roles involved: + - Which parts can a solo AI Engineer realistically own. + - Where Data Engineers, domain experts, or legal/compliance should be involved. +- 3-5 concrete example project ideas with: + - Problem description. + - Data source concept. + - What makes the dataset “unique” and valuable. + +### 4.2 End-to-end pipelines, not just API calls +- Step-by-step blueprint for going from idea → deployed system: + - Problem framing and success metrics. + - Data ingestion and transformation. + - Model/orchestration design (prompts, RAG, tools, agents). + - Evaluation, monitoring, and iteration loops. + - CI/CD, Dockerization, and cloud deployment. +- Roles and responsibilities: + - What the AI Engineer owns end-to-end. + - Where MLOps, backend, infra, or DevOps step in. +- 3-5 example end-to-end project archetypes, for example: + - Internal knowledge assistant with RAG. + - Workflow automation agent tied to company APIs. + - AI copilot inside an existing product. + +### 4.3 Real client work with documented results +- Step-by-step process to go from “I have no clients” to “I have documented outcomes”: + - Finding first clients (or pilot partners) in your network or niche communities. + - Scoping tiny but high-impact projects (1-4 weeks). + - Defining clear before/after metrics (time saved, errors reduced, revenue uplift, etc.). + - Implementing, deploying, and collecting feedback. + - Turning the work into a case study (screenshots, metrics, decisions made, lessons learned). +- Roles involved: + - How AI Engineer, client stakeholder, product owner, and sometimes data/infra support interact. +- 3-5 example “client-style” projects with: + - Domain/context. + - Clear problem statement. + - Example outcome metrics to track. + +## 5. Education & Learning Paths +Using the thesis (self-study, bootcamp, Master's, PhD) plus your own knowledge: +- Compare these pathways specifically for the *AI Engineer* role (not generic “ML engineer”). +- For each path, list: + - Time, cost, and main benefits. + - Typical failure modes. + - Who it's best for (profile, constraints). +- Propose 2-3 complete learning tracks: + - Budget/slow/indie route (heavily project and portfolio based). + - Fast but intensive route (e.g., bootcamp + aggressive portfolio building). + - Credential-heavy route (when and why a Master's or PhD actually makes sense). + +## 6. Networking & Job Strategy +Translate “Don't ask for jobs. Ask smart questions about their tech stack.” into concrete moves: +- 5-10 smart, non-generic questions to ask engineers, PMs, or founders about their AI stack. +- Examples of lightweight “spec work” or mini-demos that: + - Use their tech context. + - Respect boundaries (no overwork or exploitation). + - Show ability to build unique datasets, end-to-end pipelines, and client-style value. +- Explain how to: + - Turn these technical conversations into referrals. + - Follow up without being pushy. + +## 7. Career Timeline & Progression +Using the 6 months / 1-2 years / 3-5 years idea, define: + +- 0-6 months: + - Skills and projects focused on basic foundations and small but real tools. +- 6-12 months: + - First serious end-to-end pipelines, small unique datasets, and maybe first client-style engagements. +- 1-2 years: + - Production-ready systems, real user adoption, multiple case studies with measurable results. +- 3-5 years: + - Leading projects, mentoring, helping design AI strategy, possibly specializing (MLOps-heavy, product-heavy, or research-heavy). + +For each stage: +- What you should be able to build. +- How portfolio examples should evolve. +- How your role relative to other roles (MLOps, product, infra, etc.) typically changes. + +# OUTPUT FORMAT +Return your answer with these top-level sections: + +1. Modern AI Engineer: Definition & Scope +2. Skill Stack: Foundations, Core, Advanced +3. Surrounding Roles & Collaboration Map +4. Portfolio Engine: Unique Data, Pipelines, Client Work +5. Education & Learning Paths +6. Networking & Job Strategy +7. Career Timeline & Progression + +Within section 4, make the three sub-parts very explicit: +- 4.1 Unique Datasets Nobody Else Touched +- 4.2 End-to-End Pipelines, Not Just API Calls +- 4.3 Real Client Work with Documented Results + +Use clear headings, numbered steps, and bullet points. Aim for a document that could be used directly as: +- A career planning guide for aspiring AI Engineers, and +- A role/portfolio design reference for teams hiring AI Engineers.