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