Something strange is happening in Vietnam’s engineering hiring market right now. Companies report talent shortages. Candidates report rejection rates higher than ever. Both are telling the truth.
The gap between them isn’t a supply problem. It’s a definition problem, a fundamental mismatch between what companies say they need, what their hiring processes actually select for, and what the engineers who will matter most in the next three years actually look like.
I’ve spent a decade placing engineers across Southeast Asia. I’ve seen this kind of misalignment before. I haven’t seen it this structural.
The shift nobody fully prepared for
Eighteen months ago, the hottest debate in engineering circles was whether “Vibe Coding”, the practice of generating code entirely through natural language prompts to AI, was a legitimate workflow or a shortcut for junior developers. That debate is now over.
It turns out both sides were right, and both sides missed the point.
Yes, Vibe Coding accelerated output. A GitClear analysis of over 211 million lines of changed code found that AI-assisted workflows boosted boilerplate writing by 25–50 per cent. Yes, it also introduced a technical debt crisis: refactoring rates dropped from 25 per cent in 2021 to under 10 per cent in 2024, while duplicated code quadrupled. Over 40 per cent of junior developers admitted to deploying AI-generated code they hadn’t read.
By early 2026, Andrej Karpathy, one of the people who popularised the Vibe Coding concept, had already moved on. He began describing what comes next: Agentic Engineering. Not writing code, not prompting AI to write code, but orchestrating autonomous AI agents: setting specifications, auditing outputs, managing feedback loops, and owning architectural decisions.
The role of a software engineer is shifting from executor to decision-maker.
That shift has a direct consequence for anyone responsible for hiring.
The talent gap is real, but it’s not the gap most people think
Vietnam’s numbers are striking. The country now has 530,000–600,000 software engineers in a workforce of over 1.2 million ICT professionals. Universities produce 55,000–60,000 IT graduates per year. On paper, this looks like abundance.
In practice, demand for new technology positions exceeds 500,000 roles annually, a structural mismatch approaching 10x. And that gap is widening, not closing, for two compounding reasons.
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- First, traditional industries entered the race. Banking, retail, and manufacturing have accelerated digital transformation, competing directly with tech companies for the same engineering talent. The pool didn’t grow; the number of teams fishing in it multiplied.
- Second, semiconductors arrived. Both Ho Chi Minh City and Hanoi are now running significant semiconductor and chip-design initiatives; HCMC alone is targeting 3,000 specialist engineers, backed by programmes at ĐHQG and SHTP. Vietnam’s national target is 50,000 semiconductor engineers at university level by 2030, supported by government scholarships worth 1,300 billion VND (US$49.4 million) annually for approximately 30,000 learners.
The consequence? The best STEM graduates are no longer choosing software development as their default. The pool of candidates that tech companies relied on for the past decade is being redirected upstream.
The result: average time-to-fill for a Senior offshore engineer has stretched to 45–60 days. Retention for engineers over two years has fallen from 78 per cent in 2023 to roughly 65 per cent in 2026. Senior IT salaries have risen 50–70 per cent compared to 2024, with significant variation depending on specialisation and language ability.
The two mistakes companies are making right now
Mistake one: Hiring for the old role
Most job descriptions I see in 2026 are still optimised to find Task-Based Coders, engineers who execute well-defined tickets, follow established patterns, and stay in their lane. The interview process tests syntax, algorithms, and framework knowledge.
But the engineers who will deliver the most value in an Agentic Engineering environment are evaluated on completely different dimensions: system design judgment, the ability to audit AI-generated outputs, risk assessment, and the capacity to make independent technical decisions under pressure. These skills don’t show up on a LeetCode score.
The irony is that the very engineers companies need most are often screening out of traditional hiring pipelines, because they’ve spent recent years developing meta-skills rather than memorising framework internals.
Mistake two: Trusting language credentials over language capability
Vietnam’s tech talent market has a well-documented phenomenon I call the Paper Certificate Trap.
Language certifications, TOEIC 850+, JLPT N2, TOPIK 5, are treated as proxies for communication ability. In practice, they measure test-taking performance under controlled conditions. I have interviewed engineers with near-perfect TOEIC scores who go completely silent the moment a client asks a follow-up question in a technical meeting.
This matters because language ability is one of the strongest economic multipliers in Vietnam’s engineering market. Engineers with professional English (B2–C1), Japanese (N3–N1), or Korean (TOPIK 4–6) command salaries 30–50 per cent higher than peers with equivalent technical experience but limited to Vietnamese. A Senior AI/ML engineer with strong English can realistically earn US$3,800–US$6,000+ per month, a meaningful difference driven entirely by the ability to negotiate architecture directly with international clients.
Companies that can’t reliably identify genuine bilingual capability are paying a premium for a credential that doesn’t reflect reality, while missing engineers who have real cross-cultural communication skills but modest exam scores.
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What actually works
Move to skills-based sourcing
Replace credential screening with competency screening. Define the actual decisions and judgment calls the role requires, then design your process to surface those directly.
For senior roles in an Agentic Engineering environment, the relevant competencies are: Can this person write a system specification and defend it? Can they review a diff they didn’t write and identify the architectural implications? Can they set up a feedback loop between AI agents and quality gates?
None of these appears on a CV. All of them can be assessed in a structured technical conversation.
Implement live communication audits
For any role requiring cross-timezone collaboration or direct client contact, add a real-time communication component early in your process, not a written English test, but an actual technical conversation under mild pressure.
A 20-minute session where a candidate explains a system they’ve built, fields two or three unexpected questions, and works through a hypothetical trade-off out loud will reveal more than any certification score. Done well, this eliminates the majority of candidates who present strong paper credentials but lack genuine communication fluency, before you’ve invested weeks in technical rounds.
Match your hiring model to your actual risk profile
Not all talent gaps require the same solution, and the mid-2026 environment punishes generic approaches.
Early-stage teams prioritise flexibility over headcount permanence; access to senior expertise without long-term fixed cost is often more valuable than a full-time hire at a moment when product direction is still shifting. Growth-stage companies typically benefit from a hybrid structure: a stable core for culture and IP continuity, with flexible capacity to absorb demand spikes. Vietnam’s tech market has consistent biannual attrition cycles, with July historically the highest-churn month as mid-year reviews conclude and bonuses are paid, predictable volatility that hiring plans rarely account for.
Larger enterprises and foreign-invested companies face a different constraint: the gap between standing up a dedicated engineering function and actually integrating it. Whatever structure is chosen, the critical implementation principle is the same. Cultural and operational integration (shared tooling, CI/CD pipelines, daily standups) must begin from Day 1, not at the point of handover. Teams that delay this until a later phase consistently experience attrition at precisely the moment continuity matters most.
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The bigger picture
The scarcity that talent leaders are experiencing in 2026 is not a temporary supply shortage. It reflects a structural reclassification of what engineering capability means and a transition period where the market hasn’t yet developed reliable signals for identifying the new kind of engineer.
The companies that hire well in this environment will be the ones that invest in building those signals themselves: clearer definitions of what “decision-ready” means for their specific context, better processes for detecting genuine bilingual capability, and hiring models flexible enough to absorb the volatility of a market where the best people have more options than ever.
The companies that don’t will spend the next 18 months paying premium salaries for engineers who looked right on paper, watching their technical debt compound quietly in the background.
I’ve seen both outcomes. The difference is almost always made before the offer is signed.
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