ROLES-2026/AI-Engineers-role.prompt

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 Often outdated.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.