Tech Demand

What is worth learning · reported week 2026-W37 (last completed ISO week, SGT) · 6+ yrs · AI-ML
Experience: all0-23-56+unstated
Role: allAI-MLBackendDataFrontendFullstackMobileOther-ITPlatformSRESecurity

1. Demand ranking

16 enriched SWE postings in 2026-W37. Share = postings mentioning the technology ÷ that number — postings still awaiting enrichment are excluded from the denominator, so a processing backlog cannot depress every share at once. The chart shows the top 15; the table in section 3 lists the top 30.

machine-learning12generative-ai9aws8rag7docker6kubernetes6llm6snowflake6azure5openai5python4google-cloud3java3javascript2pytorch2

2. Momentum (vs the previous 4 weeks)

Heating up

TechnologyShareChangePostings
machine-learning75.0%+26.4pp12

Cooling down

Nothing fell this week.

Change is in percentage points of share, not relative percent: a technology going from 1 to 3 postings would otherwise read as +200% and top the board. Boards consider every technology above the bar, not only the 30 the table below shows.

3. Salary premium and entry-friendliness

Premium compares the median advertised monthly salary of postings mentioning a technology against the overall median, over the trailing 90 days. Baseline: S$12000, the median of 108 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (108 of 108, in any unit); the rest hide it, and no figure here describes them. Entry-friendly is computed over the same 90-day window. Premium mixes seniority in (senior roles name more infrastructure); pick an experience band above to compare within one. Entry-friendly = the share of postings mentioning the technology that ask for at most 2 years' experience, or are Intern/Junior roles with no stated requirement. The table lists the top 30 technologies by postings.

TechnologyKindPostingsShareSalary premiumEntry-friendly
machine-learningai1275.0% +4.2% 0.0%
generative-aiai956.2% +0.0% 0.0%
awscloud850.0% -4.2% 0.0%
ragai743.8% -6.2% 0.0%
dockertool637.5% -10.4% 0.0%
kubernetestool637.5% -8.3% 0.0%
llmai637.5% -4.2% 0.0%
snowflakedatabase637.5% —(n=8) 0.0%
azurecloud531.2% +4.2% 0.0%
openaiai531.2% -4.2% 0.0%
pythonlanguage425.0% +0.0% 0.0%
google-cloudcloud318.8% +2.1% 0.0%
javalanguage318.8% —(n=12) 0.0%
javascriptlanguage212.5% —(n=7) 0.0%
pytorchai212.5% +12.5% 0.0%
tensorflowai212.5% +12.5% 0.0%
terraformtool212.5% —(n=4) 0.0%
airflowtool16.2% —(n=6) 0.0%
cpplanguage16.2% —(n=8) 0.0%
deep-learningai16.2% —(n=18) 0.0%
expressjsframework16.2% —(n=2) 0.0%
fastapiframework16.2% —(n=11) 0.0%
github-actionstool16.2% —(n=3) 0.0%
golanguage16.2% —(n=5) 0.0%
grafanatool16.2% —(n=4) 0.0%
grpctool16.2% —(n=2) 0.0%
jenkinstool16.2% —(n=3) 0.0%
kafkatool16.2% —(n=7) 0.0%
langchainai16.2% -8.3% 0.0%
llamaai16.2% —(n=14) 0.0%

4. What else they ask for

These are MyCareersFuture's own skill tags — the competencies the employer filled in on the form, over the trailing 90 days (2026-06-23 → 2026-09-20) across 108 postings. They are not the technology ranking above: languages and frameworks appear only in the free-text description, which is why this system reads it separately. "Must-have" is the share of postings listing the tag that marked it essential rather than desirable — a tag that is everywhere but rarely essential is table stakes, one that is usually essential is a filter someone is applying.

SkillPostingsShareMarked must-have
AI Agents2725.0%3.7%
Artificial Intelligence2422.2%20.8%
Computer Science2422.2%4.2%
Ai2321.3%8.7%
LLMs1917.6%5.3%
Python1816.7%11.1%
AI Governance1715.7%5.9%
Computer Engineering1513.9%0.0%
Machine Learning1413.0%7.1%
AWS1312.0%0.0%
Data Science1312.0%15.4%
Generative AI Application Development and Deployment1312.0%7.7%
PyTorch1312.0%0.0%
AI Evaluation1211.1%0.0%
LangGraph1211.1%0.0%
Numbers computed by SQL from public MyCareersFuture data; data is refreshed daily, so it lags the live market by up to 24h. Methodology: docs/03-data-model.md · data freshness · Compliance: aggregate statistics only, no personal data.