🔄 Transitioning to AI Engineering Roles

A concrete, role-specific guide for engineers, PMs, QAs, and managers moving into AI-focused positions. No generic advice. Every path maps to real skills, real repo sections, and real courses.

Who This Guide Is For

You currently work as a software engineer, QA, PM, EM, or data engineer, and you want to move into an AI-focused role. This guide maps your existing skills to specific AI roles, tells you exactly what gaps to close, and points you to the right sections of this repo and courses to fill them.

The AI Role Landscape

Before picking a path, understand what the target roles actually are:

┌─────────────────────────────────────────────────────────────────┐
│                    AI ROLE LANDSCAPE                            │
│                                                                 │
│  ┌──────────────────────┐    ┌──────────────────────────────┐  │
│  │   APPLICATION LAYER  │    │     INFRASTRUCTURE LAYER     │  │
│  │                      │    │                              │  │
│  │  LLM App Engineer    │    │  MLOps / AI Infra Engineer   │  │
│  │  AI Product Engineer │    │  AI Platform Engineer        │  │
│  │  Agentic Systems Eng │    │  AI Reliability Engineer     │  │
│  └──────────────────────┘    └──────────────────────────────┘  │
│                                                                 │
│  ┌──────────────────────┐    ┌──────────────────────────────┐  │
│  │    QUALITY LAYER     │    │     LEADERSHIP LAYER         │  │
│  │                      │    │                              │  │
│  │  AI Eval Engineer    │    │  AI Product Manager          │  │
│  │  AI Quality Engineer │    │  AI Engineering Manager      │  │
│  │  Red Team Analyst    │    │  AI Program Manager          │  │
│  └──────────────────────┘    └──────────────────────────────┘  │
│                                                                 │
│  ┌──────────────────────┐    ┌──────────────────────────────┐  │
│  │    RESEARCH LAYER    │    │     SPECIALIST LAYER         │  │
│  │                      │    │                              │  │
│  │  Applied AI Scientist│    │  Agentic Coding Specialist   │  │
│  │  Fine-tuning Engineer│    │  RAG Architect               │  │
│  │  Alignment Researcher│    │  AI Safety Engineer          │  │
│  └──────────────────────┘    └──────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘

Transition Paths by Current Role

1. 🖥️ Backend Engineer → AI Engineering

Why backend is the best starting point: You already understand APIs, latency, databases, distributed systems, and production reliability. AI applications need all of these. The gap is mostly domain knowledge, not engineering fundamentals.

Target Roles

Backend Engineer
      │
      ├──► LLM Application Engineer       (most common transition, 3–6 months)
      ├──► Agentic Systems Engineer        (3–9 months)
      ├──► AI Infrastructure / MLOps Eng  (6–12 months, needs GPU/serving knowledge)
      └──► RAG Architect                  (4–8 months)

Skill Gap Analysis

You already have	Gap to close	Priority
REST API design	LLM API integration patterns	🔴 High
Database design	Vector databases (Qdrant, Pinecone, Weaviate)	🔴 High
Async/streaming	Streaming LLM responses, token streaming	🔴 High
Auth & multi-tenancy	Multi-tenant RAG isolation	🔴 High
Caching (Redis, CDN)	Prompt caching, semantic caching	🟡 Medium
Monitoring (Prometheus)	LLM observability (traces, evals)	🟡 Medium
CI/CD	LLMOps pipelines, model version management	🟡 Medium
N/A	Embedding models and vector math	🟡 Medium
N/A	Prompt engineering fundamentals	🟡 Medium
N/A	RAG pipeline architecture	🟡 Medium
N/A	Agent frameworks (LangGraph, CrewAI)	🟢 Lower
N/A	Fine-tuning concepts (LoRA, RLHF)	🟢 Lower

Your 90-Day Plan

Month 1: LLM Integration

Learn OpenAI / Anthropic API (streaming, function calling, structured output)
Build a simple RAG system: PDF ingestion → Qdrant → LLM response
Read this repo: 01-foundations, 02-model-landscape, 05-prompting-and-context
Course: ChatGPT Prompt Engineering for Developers (DeepLearning.AI, free)

Month 2: Production Patterns

Add multi-tenant isolation to your RAG system
Add LangSmith or Langfuse tracing
Implement prompt caching for cost savings
Read this repo: 06-retrieval-systems, 12-security-and-access, 08-memory-and-state
Course: Building and Evaluating Advanced RAG (DeepLearning.AI, free)

Month 3: Agentic Systems

Build a LangGraph agent with tools (web search, code execution)
Add evaluation pipeline with RAGAS or Phoenix
Deploy with proper cost controls and rate limiting
Read this repo: 07-agentic-systems, 09-frameworks-and-tools, 14-evaluation-and-observability
Course: AI Agents in LangGraph (DeepLearning.AI, free)

Portfolio Project Ideas

Multi-tenant document Q&A service with access control
Agentic code reviewer that posts GitHub PR comments
RAG-powered internal knowledge base with eval pipeline

2. 🎨 Frontend Engineer → AI Product Engineering

Why this transition works: Frontend engineers understand UX, real-time UI updates, and user behavior. AI products live or die on UX — streaming responses, progressive rendering, loading states, feedback collection. Your skills are more valuable than you think.

Target Roles

Frontend Engineer
      │
      ├──► AI Product Engineer         (3–6 months — highest demand)
      ├──► AI UX Engineer              (3–6 months, UX focus)
      └──► Full-Stack LLM Engineer     (6–9 months, add backend LLM skills)

Skill Gap Analysis

You already have	Gap to close	Priority
Streaming UI (SSE, WebSocket)	LLM token streaming integration	🔴 High
State management	Conversation state, session memory	🔴 High
User feedback patterns	AI feedback collection (thumbs, ratings)	🔴 High
Form validation	Prompt input validation and sanitization	🟡 Medium
Error handling for async	LLM timeout, fallback, retry patterns	🟡 Medium
A/B testing	LLM A/B testing and variant tracking	🟡 Medium
N/A	Basic prompt engineering	🟡 Medium
N/A	LLM API integration (at least one provider)	🟡 Medium
N/A	Understanding of context windows	🟡 Medium
N/A	Basic RAG concepts (what it is and why)	🟢 Lower

Your 90-Day Plan

Month 1: Integrate LLMs into UI

Build a streaming chat interface (Next.js + Vercel AI SDK)
Implement proper loading states, token-by-token rendering, error boundaries
Add a feedback widget (thumbs up/down, regenerate button)
Course: ChatGPT Prompt Engineering for Developers (DeepLearning.AI, free)

Month 2: UX Patterns for AI

Implement conversation memory with session state
Add citation rendering for RAG responses
Build a prompt playground UI for your team
Read this repo: 08-memory-and-state, 05-prompting-and-context

Month 3: Eval Integration

Instrument your UI to collect feedback signals
Connect feedback to a Langfuse or LangSmith project
Run a basic A/B test between two prompt variants
Read this repo: 14-evaluation-and-observability
Course: Evaluating and Debugging Generative AI (DeepLearning.AI + W&B, free)

Portfolio Project Ideas

Streaming document editor with AI suggestions and inline citations
Multi-step AI form wizard with persistent context
AI feedback dashboard showing per-feature quality metrics

3. 🧪 QA Engineer → AI Eval Engineer

Why QA is the most underrated path: AI evaluation is essentially a new form of QA. Manual test case design, edge case thinking, regression prevention — these are exactly what AI systems need. But the tools are different, and the mindset around non-deterministic outputs needs to shift.

Target Roles

QA Engineer
      │
      ├──► AI Eval Engineer            (3–6 months — best fit, fast transition)
      ├──► AI Quality Engineer         (3–6 months)
      └──► Red Team Analyst            (6–9 months, security focus)

Skill Gap Analysis

You already have	Gap to close	Priority
Test case design	Eval dataset creation (dimensional sampling)	🔴 High
Regression testing mindset	Eval suites as CI quality gates	🔴 High
Bug reporting	Error analysis methodology (open/axial coding)	🔴 High
Test automation	LLM-as-judge evaluator automation	🔴 High
Non-functional testing	Hallucination, bias, toxicity detection	🟡 Medium
User acceptance testing	Human annotation workflows	🟡 Medium
N/A	Tracing and observability setup	🟡 Medium
N/A	RAGAS metrics (faithfulness, relevance, recall)	🟡 Medium
N/A	Basic prompt engineering	🟢 Lower
N/A	Python scripting for eval pipelines	🟢 Lower

Your 90-Day Plan

Month 1: Error Analysis Foundation

Set up Langfuse or Phoenix tracing on any LLM application (your own or open source)
Do 3 rounds of manual error analysis: review 50 traces, write notes, categorize
Read the repo's evals companion guides:
- AI Evals: Comprehensive Study Guide
- AI Evals: LangWatch + Langfuse Guide
Course chapter to read: Error Analysis: The Secret Sauce (inside evals guides, Chapter 3)

Month 2: Build Evaluators

Write 3 code-based evaluators (JSON schema check, format validator, regex-based)
Write 1 LLM-as-judge evaluator with Train/Dev/Test calibration
Introduce judgy for statistical bias correction
Read this repo: 14-evaluation-and-observability
Course: Quality and Safety for LLM Applications (DeepLearning.AI + WhyLabs, free)

Month 3: CI/CD Integration

Wire evaluators into a GitHub Actions workflow — eval runs on every PR
Define quality gates (faithfulness > 0.85, format pass rate > 0.99)
Create a weekly eval report dashboard
Course: Evals for AI (Maven, Hamel + Shreya — paid, worth it for career transition)

Portfolio Project Ideas

Open-source eval suite for a public LLM application
Blog post: "How I applied QA methodology to catch LLM failures"
Eval pipeline template repo with LangSmith + GitHub Actions

4. 📋 Product Manager → AI Product Manager

Why PMs are uniquely positioned: AI products fail not because of bad models but because of bad product decisions (wrong problem, wrong eval criteria, wrong success metrics). PMs who understand AI failure modes are extremely rare and highly valued.

Target Roles

Product Manager
      │
      ├──► AI Product Manager           (3–6 months — direct analog)
      ├──► AI Program Manager           (3–6 months, coordination focus)
      └──► Head of AI Product           (9–18 months, leadership path)

Skill Gap Analysis

You already have	Gap to close	Priority
User research	Error analysis as voice-of-customer	🔴 High
Success metrics definition	AI-specific metrics (faithfulness, completion rate)	🔴 High
Roadmap prioritization	Failure mode prioritization from eval data	🔴 High
A/B testing	LLM A/B testing design (prompt variants, models)	🔴 High
Stakeholder communication	Explaining AI limitations to partners	🟡 Medium
PRD writing	AI system capability docs and constraint documentation	🟡 Medium
N/A	How LLMs work at a high level (no code required)	🟡 Medium
N/A	RAG pipeline concepts	🟡 Medium
N/A	Tracing / observability tools (Langfuse UI)	🟡 Medium
N/A	Prompt engineering basics	🟢 Lower

Your 90-Day Plan

Month 1: Build Technical Vocabulary

Read this repo's foundations, WITHOUT skipping to code:
- 01-foundations — understand transformers conceptually
- 02-model-landscape — know which models exist and what they cost
- GLOSSARY.md — learn the vocabulary
Course: AI for Everyone (Coursera, Andrew Ng, free) — designed for non-technical roles

Month 2: Own Error Analysis

Ask your engineering team to set up Langfuse or LangSmith
Personally review 100+ traces from your product — take notes, find patterns
Run an error analysis session with your team; lead the failure mode categorization
Read this repo: 14-evaluation-and-observability
Read: Chapter 3 (Error Analysis) in AI Evals Comprehensive Study Guide

Month 3: Define Your Eval Strategy

Write an "AI Quality Spec" for your product: define what good looks like for each feature
Work with engineers to instrument evals for those criteria
Set success metrics for your next quarter that include AI quality gates (not just user growth)
Course: Evals for AI (Maven, Hamel + Shreya — explicitly designed for PMs)

Skills That Make You Stand Out as an AI PM

You've personally reviewed traces (most PMs delegate this)
You can define failure modes quantitatively, not just qualitatively
You can communicate the cost of quality improvements (prompt changes vs. model upgrades vs. fine-tuning)
You understand the difference between RAG, fine-tuning, and prompt engineering — and when each is appropriate

5. 👨‍💼 Engineering Manager → AI Engineering Manager

The EM transition is about leadership evolution: Technical literacy in AI is necessary but not sufficient. The key shift is managing non-deterministic systems, teams evaluating quality without ground truth, and a field that changes every 3–6 months.

Target Roles

Engineering Manager
      │
      ├──► AI Engineering Manager       (6–12 months)
      ├──► Director of AI Engineering   (12–24 months)
      └──► VP of AI / Head of AI        (18–36 months)

What Changes as an AI EM

Traditional EM	AI EM additions
Sprint planning	Eval-driven iteration cycles
PR review standards	Eval suite as the new "tests pass" bar
Hiring for backend/frontend	Hiring for LLM, vector search, evals expertise
Incident response for outages	Incident response for quality regressions
Roadmap with feature flags	Roadmap with model upgrade risks
Performance reviews based on delivery	Performance reviews including AI quality ownership

Your 90-Day Plan

Month 1: Technical Depth

Read all of 09-frameworks-and-tools to understand the tooling landscape
Read 09-claude-code.md and 10-opencoderguide.md — you'll manage teams using these
Understand costs: read 02-model-landscape/03-pricing-and-costs.md
Course: Generative AI with LLMs (Coursera, DeepLearning.AI) — gives you enough depth to lead technical discussions

Month 2: Process and Team Design

Redesign your team's definition of "done" to include eval gates
Build an eval culture: weekly trace reviews, quality metrics in retros
Define your AI incident runbook: what happens when hallucination rate spikes?
Read this repo: 13-reliability-and-safety, 14-evaluation-and-observability

Month 3: Strategy and Hiring

Define the AI skills matrix for your team: who has what, what's missing
Build an interview rubric for AI engineers (use 00-interview-prep as your source)
Set team-level AI quality OKRs for next quarter
Course: CS294 LLM Agents (Berkeley, free) — gives you the depth for strategy conversations

6. 🛠️ DevOps / Platform Engineer → MLOps / AI Infrastructure Engineer

Why platform engineers thrive here: Kubernetes, CI/CD, observability, cost management, SLAs — you've done all of this. The AI-specific additions are GPU scheduling, model serving, and LLMOps pipelines.

Target Roles

DevOps / Platform Engineer
      │
      ├──► MLOps Engineer               (3–6 months)
      ├──► AI Infrastructure Engineer   (6–9 months)
      └──► AI Platform Engineer         (9–12 months)

Skill Gap Analysis

You already have	Gap to close	Priority
Container orchestration (K8s)	GPU node pools, NVIDIA device plugins	🔴 High
CI/CD pipelines	LLMOps pipelines (model eval, deployment gates)	🔴 High
Observability stacks	LLM-specific metrics (token throughput, TTFT)	🔴 High
Cost management	GPU cost optimization, spot instances for training	🔴 High
Secret management	API key rotation for multiple LLM providers	🟡 Medium
N/A	vLLM / TGI for self-hosted model serving	🟡 Medium
N/A	Model versioning and registry	🟡 Medium
N/A	Quantization basics (GPTQ, AWQ, GGUF)	🟡 Medium
N/A	Basic prompt engineering to understand what you're serving	🟢 Lower

Your 90-Day Plan

Month 1: LLM Serving

Deploy vLLM locally serving Llama 3.3 7B or Qwen2.5-Coder
Add Prometheus metrics: tokens/sec, latency P50/P95/P99, queue depth
Set up auto-scaling based on request queue
Read this repo: 04-inference-optimization, 11-infrastructure-and-mlops
Course: Efficiently Serving LLMs (DeepLearning.AI + Predibase, free)

Month 2: LLMOps Pipeline

Set up LangSmith or Langfuse for trace collection
Build a CI/CD quality gate: eval suite runs before model deploy
Implement prompt version control (Langfuse prompt registry or DSPy)
Read this repo: 14-evaluation-and-observability

Month 3: Scale and Cost

Compare self-hosted vs. API cost at target volume (use pricing guide in repo)
Set up cost dashboards per model, per team, per feature
Implement graceful multi-provider failover
Course: ML Engineering for Production (MLOps) (Coursera, DeepLearning.AI)

7. 📊 Data Engineer → AI Data / Feature Engineer

Why data engineers are essential: Training data is the competitive moat of AI systems. Data pipelines, quality, and freshness determine model performance more than architecture. Your skills are immediately applicable.

Target Roles

Data Engineer
      │
      ├──► AI Data Engineer             (2–4 months — fastest transition)
      ├──► Embedding Pipeline Engineer  (3–6 months)
      └──► Fine-tuning Data Specialist  (4–8 months)

Skill Gap Analysis

You already have	Gap to close	Priority
ETL pipelines	Document ingestion pipelines for RAG	🔴 High
Data quality checks	Eval dataset quality validation	🔴 High
Schema design	Metadata schema for vector databases	🔴 High
Streaming pipelines	Real-time embedding and index update	🟡 Medium
N/A	Embedding model selection and batching	🟡 Medium
N/A	Vector database operations (upsert, filter, ANN search)	🟡 Medium
N/A	Chunking strategies for document types	🟡 Medium
N/A	Annotation pipeline design for fine-tuning	🟢 Lower
N/A	RLHF preference data format	🟢 Lower

Your 90-Day Plan

Month 1: RAG Data Pipeline

Build an ingestion pipeline: PDF/HTML/DOCX → chunked → embedded → Qdrant
Add data quality gates: minimum chunk size, deduplication, language detection
Implement incremental sync: only re-embed changed documents
Read this repo: 06-retrieval-systems/02-chunking-strategies.md, 10-document-processing

Month 2: Eval Dataset Engineering

Build a test dataset using dimensional sampling (see evals guides)
Set up human annotation pipeline using Label Studio or Argilla
Track inter-annotator agreement; reject low-quality labels
Read: AI Evals Comprehensive Study Guide, Chapter 12 (Human Annotation)
Course: Finetuning Large Language Models (DeepLearning.AI, free)

Month 3: Advanced Data Engineering

Build a pipeline that turns production traces into fine-tuning examples
Implement embedding drift detection: alert when document distribution shifts
Benchmark 3 embedding models on your domain data
Read this repo: 03-training-and-adaptation

📊 Role Comparison Overview

Role            Months to   Avg Salary    Best Suited For
                First Role   (US, 2026)
────────────────────────────────────────────────────────
Backend         3–6 mo       $170–220K    LLM App / Agentic Engineering
Frontend        3–6 mo       $150–190K    AI Product / UX Engineering
QA              3–6 mo       $140–180K    AI Eval / Quality Engineering
PM              3–6 mo       $160–200K    AI Product Management
DevOps          3–6 mo       $170–220K    MLOps / AI Platform
Data Eng        2–4 mo       $165–210K    RAG Data, Fine-tuning Data
EM              6–12 mo      $200–280K    AI Engineering Manager

Salaries are US market estimates based on Levels.fyi and LinkedIn data, May 2026. Ranges vary significantly by company, location, and experience level.

🗺️ Which Repo Sections Map to What

Use this when you're ready to go deep:

Topic	Repo Section	Why
How LLMs work	01-foundations	Foundation for everything else
Which model to use	02-model-landscape	Model selection is a daily decision
Fine-tuning	03-training-and-adaptation	For fine-tuning data specialist path
GPU serving / vLLM	04-inference-optimization	MLOps / Platform path
Prompt engineering	05-prompting-and-context	Everyone needs this
RAG pipeline	06-retrieval-systems	Backend / Data Eng path
Agentic systems	07-agentic-systems	Backend / Senior AI Eng path
Memory & state	08-memory-and-state	All engineers building agents
LangGraph, CrewAI, Claude Code	09-frameworks-and-tools	Practical tool selection
Document parsing	10-document-processing	Data Eng / RAG path
GPU infra, LLMOps	11-infrastructure-and-mlops	DevOps / Platform path
Multi-tenant security	12-security-and-access	Backend / PM path
Guardrails, red teaming	13-reliability-and-safety	QA / Red Team path
RAGAS, LangSmith, evals	14-evaluation-and-observability	QA / PM / all roles
Design patterns	15-ai-design-patterns	Senior level preparation
Case studies	16-case-studies	Interview prep, reference designs
Evals deep dive	AI Evals Comprehensive Guide	QA / PM path
AI Evals (LangWatch)	AI Evals LangWatch Guide	QA / Eval Eng path
Interview prep	00-interview-prep	All roles
Courses	COURSES.md	All roles

📚 Recommended Starter Courses by Role

Full details in COURSES.md

Your Role	First Course	Second Course	Third Course
Backend	ChatGPT Prompt Engineering for Devs (DL.AI, free)	Building & Evaluating RAG (DL.AI, free)	AI Agents in LangGraph (DL.AI, free)
Frontend	ChatGPT Prompt Engineering for Devs (DL.AI, free)	Building Systems with ChatGPT API (DL.AI, free)	Evaluating & Debugging GenAI (DL.AI + W&B, free)
QA	AI Evals Guide in this repo (free)	Quality & Safety for LLM Apps (DL.AI, free)	Evals for AI – Maven (Hamel + Shreya, paid)
PM	AI for Everyone (Coursera, free)	AI Evals Guide Chapter 3 (free)	Evals for AI – Maven (Hamel + Shreya, paid)
DevOps	Efficiently Serving LLMs (DL.AI, free)	Evaluating & Debugging GenAI (DL.AI + W&B, free)	ML Engineering for Production (Coursera)
Data Eng	Building & Evaluating RAG (DL.AI, free)	Finetuning LLMs (DL.AI, free)	AI Evals Guide in this repo (free)
EM	Generative AI with LLMs (Coursera)	AI Agents in LangGraph (DL.AI, free)	CS294 LLM Agents (Berkeley, free)

DL.AI = DeepLearning.AI

Common Mistakes to Avoid

Skipping fundamentals — Jumping to LangChain before understanding what an embedding is leads to cargo-cult code you can't debug.
Building before evaluating — Ship nothing without a way to measure quality. Define your eval criteria before writing the first prompt.
Copying prompts without understanding them — Prompts are engineering decisions. Understand why each element is there.
Ignoring costs until it's too late — Every API call has a price. Build cost tracking from day one. See 02-model-landscape/03-pricing-and-costs.md.
Assuming the model is the bottleneck — In most production AI systems, the bottleneck is retrieval quality, prompt design, or data quality. The model is rarely the problem.
Using "latest" in model version strings in production — Pin exact versions. Silent model updates will break your product.
Over-agenting — Starting with a 5-agent system when a single well-prompted call would work. Start simple, add complexity only when needed.

How to Get Hired

Build in public. The AI engineering job market rewards demonstrated work:

GitHub portfolio: One polished end-to-end project beats 10 toy projects
Write a blog post: Describing one real problem you solved and how (error analysis, eval pipeline, RAG latency fix)
Contribute to open source: OpenHands, LlamaIndex, DSPy, RAGAS. Even documentation PRs get you noticed.
Use this repo's interview prep: 00-interview-prep/01-question-bank.md has 128 questions with strong answers

What to say in interviews:

Name specific decisions: "I chose Qdrant over Pinecone because of X" (not "I built a RAG system")
Cite failure modes you've encountered and how you fixed them
Know at least one benchmark by heart (SWE-bench, RAGAS scores, TTFT for your serving setup)
Show you think about eval and cost, not just features

Part of the AI System Design Guide — maintained by ombharatiya

Who This Guide Is For​

The AI Role Landscape​

Transition Paths by Current Role​

1. 🖥️ Backend Engineer → AI Engineering​

Target Roles​

Skill Gap Analysis​

Your 90-Day Plan​

Portfolio Project Ideas​

2. 🎨 Frontend Engineer → AI Product Engineering​

Target Roles​

Skill Gap Analysis​

Your 90-Day Plan​

Portfolio Project Ideas​

3. 🧪 QA Engineer → AI Eval Engineer​

Target Roles​

Skill Gap Analysis​

Your 90-Day Plan​

Portfolio Project Ideas​

4. 📋 Product Manager → AI Product Manager​

Target Roles​

Skill Gap Analysis​

Your 90-Day Plan​

Skills That Make You Stand Out as an AI PM​

5. 👨‍💼 Engineering Manager → AI Engineering Manager​

Target Roles​

What Changes as an AI EM​

Your 90-Day Plan​

6. 🛠️ DevOps / Platform Engineer → MLOps / AI Infrastructure Engineer​

Target Roles​

Skill Gap Analysis​

Your 90-Day Plan​

7. 📊 Data Engineer → AI Data / Feature Engineer​

Target Roles​

Skill Gap Analysis​

Your 90-Day Plan​

📊 Role Comparison Overview​

🗺️ Which Repo Sections Map to What​

📚 Recommended Starter Courses by Role​

Common Mistakes to Avoid​

How to Get Hired​

Who This Guide Is For

The AI Role Landscape

Transition Paths by Current Role

1. 🖥️ Backend Engineer → AI Engineering

Target Roles

Skill Gap Analysis

Your 90-Day Plan

Portfolio Project Ideas

2. 🎨 Frontend Engineer → AI Product Engineering

Target Roles

Skill Gap Analysis

Your 90-Day Plan

Portfolio Project Ideas

3. 🧪 QA Engineer → AI Eval Engineer

Target Roles

Skill Gap Analysis

Your 90-Day Plan

Portfolio Project Ideas

4. 📋 Product Manager → AI Product Manager

Target Roles

Skill Gap Analysis

Your 90-Day Plan

Skills That Make You Stand Out as an AI PM

5. 👨‍💼 Engineering Manager → AI Engineering Manager

Target Roles

What Changes as an AI EM

Your 90-Day Plan

6. 🛠️ DevOps / Platform Engineer → MLOps / AI Infrastructure Engineer

Target Roles

Skill Gap Analysis

Your 90-Day Plan

7. 📊 Data Engineer → AI Data / Feature Engineer

Target Roles

Skill Gap Analysis

Your 90-Day Plan

📊 Role Comparison Overview

🗺️ Which Repo Sections Map to What

📚 Recommended Starter Courses by Role

Common Mistakes to Avoid

How to Get Hired