✍️ 필사 모드: The Complete AI Engineer Career Guide — From Junior to Principal: Leveling, Interviews, Portfolio, Compensation, Remote, and the Next Decade (2025-2026)

"Your career is your most important product. Treat it like one." — Reid Hoffman

In the previous post [AI Engineering in Production] I covered how to build. Now it's time for how you grow.

AI Engineer has taken shape rapidly over 2024-2025, after Chip Huyen's 2023 definition. The traditional ML Engineer path was PhD- and paper-centric, but AI Engineer is a new track you can enter from an SWE background. The career map, however, isn't standardized yet. This post draws that map directly.

Intended readers:

• Junior AI Engineers who just entered the field or are pivoting in.
• Senior backend/full-stack engineers considering a pivot to AI Engineer.
• Seniors preparing to step into the Staff/Principal track.

Table of Contents

1. AI Engineer leveling framework — L3 to L8
2. Junior (L3-L4) — Years 0-3: Foundations
3. Senior (L5) — Years 3-6: System design
4. Staff (L6) — Years 6-10: Organizational impact
5. Principal (L7-L8) — Technical strategy
6. ML Engineer track vs AI Engineer track
7. Interview structure analysis — as of 2025
8. Portfolio strategy — 5 patterns
9. Compensation — Korea, US, and remote
10. Remote and global job strategy
11. AI Engineer in 10 years — 5 scenarios
12. Checklist and anti-patterns

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1. AI Engineer Leveling Framework

1.1 Cross-company level comparison

1.2 Five axes of AI Engineer competency

1. Technical depth — internals of LLM, RAG, and agent systems.
2. System design — production architecture at scale.
3. Product sense — connecting to users and the business.
4. Communication — technical writing, talks, cross-team.
5. Leadership — mentoring, decision-making, organizational impact.

As you climb, it's not just technical depth that matters — the other four become exponentially more important.

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2. Junior (L3-L4) — Years 0-3: Foundations

2.1 Expected competencies

• Implement well-scoped tasks solo.
• Take code review actively and incorporate feedback.
• Build prompts, RAG, and simple agents.
• Fundamentals in Python, Git, Docker, and cloud.

2.2 Must-dos

1. Implement 10 LLM API features from scratch: chatbot, RAG, agent, tool use, structured output, streaming, function calling, embeddings, reranking, evals.
2. Learn by reading open source: the internals of LangChain, LlamaIndex, vLLM.
3. Habitual paper reading: one a week. GPT-3, Chinchilla, Constitutional AI, DPO, RAG.
4. Absorb eval culture: on your first production deployment, build an eval harness without fail.

2.3 Don'ts

• "Just using LLM wrappers" — using without understanding internals.
• Total avoidance of GPUs and math — at minimum, understand linear algebra and probability.
• Locking into one framework — only LangChain, only OpenAI.
• Insisting on solo learning — missing PR review and pair programming opportunities.

2.4 What to prove by year 3

• Ship at least one production LLM feature from zero to one.
• 3-5 technical blog posts.
• 3+ open-source PRs (even small ones).

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3. Senior (L5) — Years 3-6: System Design

3.1 Expected competencies

• 0 to 1 design: requirements → architecture → implementation → operations, end to end.
• Cross-team coordination: PM, Design, Data, Infra.
• Mentoring juniors: code review, onboarding, pairing.
• Trade-off decisions: RAG vs fine-tuning, vendor selection, cost vs quality.

3.2 What senior interviews probe

1. Design review — "Design a document Q&A system handling 10M queries per month."
2. Incident — "An LLM hallucinated an answer in prod. How do you respond?"
3. Cost — "Cut a $100K/month bill down to $20K/month."
4. Eval strategy — "How do you structure evals before shipping a new model to prod?"

3.3 Common mistakes at senior

• Too deep in tech — weak link to business impact.
• Under-documented — design exists only in your head.
• Disinterest in promotion politics — failure to make impact visible.
• Learning plateau — coasting on the first product win.

3.4 Preparing for staff

• Write 3-5 Design Docs or RFCs.
• Lead one company-wide problem: e.g., a company-wide LLM gateway platform.
• External visibility: conference talks, technical blog.

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4. Staff (L6) — Years 6-10: Organizational Impact

4.1 Expected competencies — Tanya Reilly's four

From Tanya Reilly's The Staff Engineer's Path:

1. Big-picture thinking — understanding org and industry terrain.
2. Execution — turning ambiguous problems into outcomes.
3. Leveling up — growing people around you.
4. Influence without authority — driving change without direct command.

4.2 What a staff AI engineer actually does

• Helps shape company-wide AI strategy.
• Platform design: shared infra like Eval, Prompt, MCP.
• Leading complex incidents that span multiple teams.
• Hiring and hiring committee: leveling decisions.
• Multiplying teammates: 5 people made 2x effective is 10x in aggregate.

4.3 Staff archetypes (Will Larson, Staff Engineer)

1. Tech Lead — team-level technical leader.
2. Architect — architecture-focused.
3. Solver — takes on problems the company can't solve elsewhere.
4. Right Hand — the exec's technical partner.

In AI Engineering, the most common hybrid is Tech Lead + Solver.

4.4 What staff must focus on

• Tech debt vs new features balance.
• Build vs Buy: Weaviate vs home-grown vector search.
• Managing vendor lock-in: the risk of going all-in on OpenAI.
• Org culture: eval-driven, review culture.

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5. Principal (L7-L8) — Technical Strategy

5.1 Expected competencies

• Setting company direction — technical strategy alongside CEO/CTO.
• Industry leadership — standards, open source, public talks.
• Talent magnet — people join because you're there.
• Long-horizon bets — 3-5 year R&D.

5.2 A principal's day

• Half: meetings, reviews, decisions.
• A quarter: strategy documents.
• A quarter: deep technical immersion (experiments in new paradigms, prototypes).

5.3 Are principals made?

• Formal promotion paths are rare — mostly the result of accumulated tenure.
• Two or more staff stints is common.
• External reputation is non-negotiable — the industry has to know your name.
• Founder or CTO pivots are a common route.

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6. ML Engineer Track vs AI Engineer Track

6.1 ML Engineer path

• PhD or MS with papers and research.
• Feature engineering, classical ML plus DL.
• Foundation model training and research.
• Big labs: Google Brain, FAIR, DeepMind, Apple MLR.
• Scale AI, Anthropic, OpenAI Research.

6.2 AI Engineer path

• SWE background plus LLM composition.
• Defined by Chip Huyen in 2023.
• The majority of most company AI teams today.
• Low entry bar, fierce competition.

6.3 Hybrid strategy

After 2026, the MLE/AIE boundary will blur.

• AIEs will go deeper into fine-tuning, embeddings, and evals.
• MLEs will have to ship agents and productionize.
• "Full-stack AI Engineer" is the aspirational label.

6.4 Research vs Applied

• Research — papers, novel algorithms. PhD usually expected.
• Applied — shipping products. PhD not required.
• Research Engineer — in between. Experiment scale, infra.

AI Engineer sits on the Applied side. If you want to do research, Research Scientist or Research Engineer fits better.

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7. Interview Structure Analysis — 2025

7.1 A typical AI Engineer loop: 5-7 rounds

1. Recruiter screen — background, motivation.
2. Hiring manager — experience, fit.
3. Coding — medium LeetCode plus Python fluency.
4. ML/LLM fundamentals — transformer, attention, RAG internals.
5. System design — LLM system design.
6. Behavioral — STAR-based.
7. Bar raiser / exec — culture, cross-functional.

7.2 ML fundamentals they always ask

• Attention mechanism — Q, K, V math and why we divide by sqrt(d_k).
• Transformer architecture — encoder, decoder, decoder-only differences.
• Tokenization — BPE, SentencePiece, why Korean produces more tokens.
• Sampling — temperature, top-k, top-p, beam search.
• Fine-tuning — full vs LoRA vs RLHF vs DPO.
• RAG internals — embedding similarity, reranking, chunking.
• Evaluation — limits of BLEU/ROUGE, LLM-as-Judge bias.

7.3 System design staples

• "Design an AI assistant that replies in Slack" (multi-tenant, PII, retention).
• "Automated code review commenter" (repo-level context, PR diff).
• "Real-time video meeting summaries" (ASR + LLM + streaming).
• "Multilingual customer support bot" (language detection, fallback, escalation).

7.4 Behavioral (STAR)

• Conflict — how you resolved a technical disagreement with a peer.
• Failure — how you responded to and retrospected on a prod incident.
• Influence — times you led change without authority.
• Ambiguity — when requirements weren't clear.

7.5 Online assessment trends

• CoderPad + OpenAI API — a mini-project in 60-90 minutes.
• HackerRank ML — numerics, matrices, preprocessing.
• Take-home — 2-5 days, build a real LLM app.

Take-homes eat time but are the best way to prove capability.

7.6 Interview prep resources

• Deep Learning Interviews (Kalevi Kilkki) — LLM questions.
• Designing Machine Learning Systems (Chip Huyen) — system design.
• Machine Learning Interviews (Chip Huyen) — interview guide.
• LeetCode + NeetCode — the canonical 150.
• Mock interviews — interviewing.io, Pramp.

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8. Portfolio Strategy — 5 Patterns

8.1 Why portfolio is decisive

The AI Engineer market is flooded with 1-3 year applicants. A one-page resume can't separate you. Your work does.

8.2 Five portfolio patterns

1. End-to-end apps — RAG chatbot, code review bot, email agent. Deploy on Vercel or Fly.io.
2. Open-source contributions — 3+ PRs to LangChain, LlamaIndex, or vLLM.
3. Paper reimplementations — nanoGPT-style: attention, RLHF, RAG, DPO.
4. Technical blog — 1-2 posts a month. Deep analysis or experiments.
5. Kaggle and competitions — LLM RAG contests, AI Mathematical Olympiad.

8.3 A good GitHub README

• Problem statement — why you built it.
• Architecture diagram.
• How to run — one-click demo link.
• Limitations and learnings — what didn't work.
• Evals — with numbers.

8.4 Portfolios to avoid

• Tutorial copies — "To-Do app with LangChain" level.
• Not runnable — README only, no working code.
• Too small (single file) or too bloated (1000 commits, no refactor).

8.5 Technical blogging strategy

• Substack, Medium, or your own blog (Next.js + MDX).
• Cross-post to LinkedIn.
• Core subjects: failure stories, benchmark comparisons, production case studies.
• Benchmark against Hamel Husain, Chip Huyen, Jason Liu.

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9. Compensation — Korea, US, and Remote

9.1 Korea (as of 2025)

9.2 US (2025, per Levels.fyi)

OpenAI and Anthropic are in an extreme competitive cycle right now, with $1M+ TC common at L5 and above.

9.3 Global remote (based in Korea)

• Vercel, Supabase, Linear, Replicate, Modal — remote-first.
• Korea-based TC: $150K-$350K (stock included).
• Tax structure — be careful about entry/residency around stock.

9.4 Understanding comp structure

• Base salary — fixed, monthly.
• Bonus — 10-25% per year.
• RSU (Restricted Stock) — 4-year vest, 1-year cliff.
• Options — startup land. 83(b) election.
• Sign-on — a switching incentive.
• Refresh grant — additional RSU each year.

9.5 Negotiation tips

• Competing offer is the strongest lever.
• Ground yourself in Levels.fyi + Blind data.
• Base vs Stock vs Sign-on ratio is negotiable.
• Combat-readiness with recruiters — the craft of asking and staying silent.

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10. Remote and Global Job Strategy

10.1 Why try remote first

• No relocation risk.
• Start without a visa.
• English and communication training.
• Easier to relocate later on top of that.

10.2 Remote-friendly companies

Fully remote-first:

• Vercel, Supabase, Linear, Replicate, Modal.
• GitLab, Zapier, HashiCorp, Automattic.
• Ghost, Deel, Remote.com.

Hybrid but internationally hiring:

• Anthropic (selective), Scale AI, Weights & Biases.

10.3 Visas and relocation

• US H-1B — lottery. O-1 (extraordinary ability) is more realistic.
• UK Skilled Worker — points-based.
• Germany Blue Card — minimum salary threshold.
• Canada Express Entry / Global Talent Stream.
• Japan HSP — points-based, favorable to Koreans.
• Singapore Tech.Pass / EP.

10.4 Language and culture prep

• English writing — RFCs, Slack, PRs. Writing before speaking.
• Async communication — timezone-aware, documented asynchronously.
• Timezone politics — European companies rarely need Korea-dawn meetings. US companies will demand Korea-night time.

10.5 Designing a Korea-based global career

1. Korean startup with a global product — Sendbird, Channel Talk, Upstage.
2. Overseas branches of Korean giants — Naver US, Kakao Japan.
3. Global remote full-time.
4. Contractor / consulting — agencies like Deel or Remote.com.
5. Relocation.

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11. AI Engineer in 10 Years — 5 Scenarios

11.1 Scenario 1: "AI Full-stack Engineer" as the default

Role specialization stabilizes, and AI Engineer becomes the standard title for full-stack SWE plus LLM expertise. By the mid-2030s, 70% of SWEs hold this identity.

11.2 Scenario 2: "Vertical AI Specialist" split

Domain-specialized AI Engineers in law, medicine, and finance take the lead. The generalist AI Engineer market gets commoditized. Domain depth becomes the main driver of comp.

11.3 Scenario 3: "AI Ops Engineer" and "AI Product Engineer" split

• AI Ops — specialized in infra, cost, eval, guardrails.
• AI Product — specialized in UX, prompts, agent design.
• The two tracks clearly separate.

11.4 Scenario 4: AI replaces AI Engineers

Agents grow smart enough to automate 70% of AI Engineer work. What remains: system design, ethical judgment, translating with domain experts. Demand for human AI Engineers falls, but price per head skyrockets.

11.5 Scenario 5: Post-AGI restructuring

If AGI arrives by 2030-2035, traditional engineering careers get redefined. Physical-world integrations — robotics, energy, bioengineering — become the last frontier for human engineers.

11.6 How to prepare

• Low-level systems + AI combos: robotics, embedded, security.
• Domain vertical depth: law, medicine, bio, energy.
• Leadership: the triangle of tech, business, ethics.
• Founder mindset.

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12. Burnout and Long-term Pacing

12.1 AI Engineers are especially burnout-prone

• Explosive information volume — new models and techniques every week.
• Hype-driven — PR pressure driven by weekend tweets.
• Over-investment: companies push for outcomes.

12.2 Defensive strategies

• Cap RSS/Twitter scanning to 1-2 sessions a week — the rest is deep work.
• 2-4 weeks of vacation a year.
• Keep side projects in a different register from day job.
• Health foundation: 7h sleep, 3x/week exercise.

12.3 Sustaining across 5 and 10 years

• A settled learning engine (see prior post).
• Side projects → side income → founder optionality.
• Relationship capital: former teammates, mentors, mentees.
• Financial basics: 6-month emergency fund, low-risk investing.

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Checklist

Is my career actually well-designed?

1. ☐ I know my current level (L3/L4/L5/...) against my company's official criteria.
2. ☐ I've explicitly agreed on next-level expectations with my manager.
3. ☐ I know my market comp from Levels.fyi and Blind.
4. ☐ My GitHub profile has 3 projects a visitor can actually understand.
5. ☐ I publish at least one technical blog or LinkedIn post per quarter.
6. ☐ I review system design, coding, and ML fundamentals quarterly for interview readiness.
7. ☐ I have at least one remote/overseas option in the pipeline.
8. ☐ I have one mentor and one mentee.
9. ☐ I take at least 2 weeks of rest per year.
10. ☐ I have 6 months of emergency cash.
11. ☐ I've drawn three concrete 10-year scenarios for myself.
12. ☐ I've chosen my domain vertical depth (law, medicine, finance, etc.).

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10 Common Anti-patterns

1. Not having the promotion conversation with your manager first — there's no "just do good work and it'll come."
2. Job-hopping only to raise comp — hard to accumulate 3-5 year impact.
3. Chasing only the latest tools and frameworks — system thinking and soft skills stagnate.
4. Trying to switch without a portfolio — the resume alone caps you.
5. Expecting the global market without improving English.
6. Failing to self-assess — no Levels.fyi or Blind check.
7. Sacrificing health for short-term results — burnout 2-3 years later.
8. No mentor — the cost of trial and error is enormous.
9. Ignoring community — your network decides your next move.
10. Pessimism that "AI will replace everything" — people still make the difference today.

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Next post — "Designing Influence for Senior and Staff Engineers: Technical Writing 2.0, Talks, Conferences, Open Source, and Tech Leadership Branding"

The reason technically strong seniors and staff don't grow further is the absence of influence design.

• Technical Writing 2.0 — from RFC to external and eng blog
• Conference talks — QCon, KubeCon, DockerCon prep
• Life as an open-source maintainer
• Personal brand vs company brand
• Mentor and advisor network
• Transitioning from internal Staff to external Staff

Your career can only be designed as far as your awareness goes. Continued in the next post.