13 enriched SWE postings in 2026-W44. 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.
No technology cleared the momentum bar this week (10+ postings under this lens): with counts that thin, a share delta would read noise as trend, so the boards stay empty rather than calling the market flat.
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 154 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (154 of 154, 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.
| Technology | Kind | Postings | Share | Salary premium | Entry-friendly |
|---|---|---|---|---|---|
| machine-learning | ai | 8 | 61.5% | +0.0% | 0.0% |
| python | language | 8 | 61.5% | +0.0% | 0.0% |
| generative-ai | ai | 7 | 53.8% | +4.2% | 0.0% |
| rag | ai | 7 | 53.8% | -4.2% | 0.0% |
| aws | cloud | 6 | 46.2% | +0.0% | 0.0% |
| llm | ai | 6 | 46.2% | +0.0% | 0.0% |
| azure | cloud | 5 | 38.5% | +12.5% | 0.0% |
| deep-learning | ai | 5 | 38.5% | +12.5% | 0.0% |
| pytorch | ai | 5 | 38.5% | +16.7% | 0.0% |
| google-cloud | cloud | 4 | 30.8% | +6.2% | 0.0% |
| nlp | ai | 4 | 30.8% | +12.5% | 0.0% |
| tensorflow | ai | 4 | 30.8% | +16.7% | 0.0% |
| git | tool | 3 | 23.1% | +0.0% | 0.0% |
| grafana | tool | 3 | 23.1% | —(n=11) | 0.0% |
| kubernetes | tool | 3 | 23.1% | -8.3% | 0.0% |
| openai | ai | 3 | 23.1% | +8.3% | 0.0% |
| prometheus | tool | 3 | 23.1% | —(n=11) | 0.0% |
| terraform | tool | 3 | 23.1% | —(n=14) | 0.0% |
| computer-vision | ai | 2 | 15.4% | —(n=7) | 0.0% |
| docker | tool | 2 | 15.4% | -12.5% | 0.0% |
| github-actions | tool | 2 | 15.4% | —(n=8) | 0.0% |
| jenkins | tool | 2 | 15.4% | —(n=5) | 0.0% |
| sql | language | 2 | 15.4% | +0.0% | 0.0% |
| airflow | tool | 1 | 7.7% | —(n=7) | 0.0% |
| cpp | language | 1 | 7.7% | —(n=9) | 0.0% |
| datadog | tool | 1 | 7.7% | —(n=8) | 0.0% |
| gitlab-ci | tool | 1 | 7.7% | —(n=1) | 0.0% |
| go | language | 1 | 7.7% | —(n=6) | 0.0% |
| java | language | 1 | 7.7% | +6.2% | 0.0% |
| javascript | language | 1 | 7.7% | —(n=15) | 0.0% |
These are MyCareersFuture's own skill tags — the competencies the employer filled in on the form, over the trailing 90 days (2026-08-08 → 2026-11-05) across 154 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.
| Skill | Postings | Share | Marked must-have |
|---|---|---|---|
| Artificial Intelligence | 40 | 26.0% | 10.0% |
| Computer Science | 36 | 23.4% | 2.8% |
| AI Agents | 34 | 22.1% | 2.9% |
| Ai | 34 | 22.1% | 5.9% |
| Python | 31 | 20.1% | 9.7% |
| AI Governance | 30 | 19.5% | 6.7% |
| Machine Learning | 30 | 19.5% | 3.3% |
| Data Science | 29 | 18.8% | 13.8% |
| PyTorch | 24 | 15.6% | 0.0% |
| TensorFlow | 21 | 13.6% | 4.8% |
| Computer Engineering | 20 | 13.0% | 0.0% |
| Design | 20 | 13.0% | 0.0% |
| LLMs | 19 | 12.3% | 5.3% |
| AWS | 17 | 11.0% | 0.0% |
| Responsible AI | 16 | 10.4% | 0.0% |