model deployment jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-04, model deployment appears in 1,086 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning model deployment, with demand share down 9.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-04
Which roles want model deployment?
Upload your resume and Skillenai will show which roles your model deployment experience fits, which skills you already cover, and what is missing.
Frequently asked questions about model deployment
+Is model deployment in demand in 2026?
Yes. model deployment appears in 1,086 job postings indexed by Skillenai over the 90 days ending 2026-11-04. Data Scientist accounts for the most postings mentioning model deployment (16.7% of all postings mentioning model deployment).
+What jobs require model deployment?
According to the Skillenai jobs index over the 90 days ending 2026-11-04, among roles with at least 20 postings, the highest shares mentioning model deployment are ML Platform Engineer (28.0% of that role’s postings mention model deployment), Field Application Engineer (18.2% of that role’s postings mention model deployment), Applied Value Engineer (17.2% of that role’s postings mention model deployment).
+What skills are commonly paired with model deployment?
Across job postings indexed by Skillenai (90 days ending 2026-11-04), model deployment most often appears alongside Python, machine learning, MLOps, model monitoring, SQL.
+Where is model deployment most in demand?
As of 2026-11-04, the metro areas posting the most jobs requiring model deployment are London, San Francisco, New York City, Bengaluru, San Jose, according to the Skillenai jobs index.
+How can I keep up with new model deployment content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning model deployment alongside the jobs index. You can subscribe to a daily email digest of new model deployment content from your Skillenai account.
+Which skills come before and after model deployment?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring model deployment — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around model deployment
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before model deployment
Skills after model deployment
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: python | 8 |
| Before: Tableau | 5 |
| Before: Power BI | 4 |
| Before: sql | 4 |
| Before: data analysis | 4 |
| Before: matplotlib | 4 |
| Before: anomaly detection | 4 |
| Before: Random Forest | 3 |
| After: python | 4 |
| After: scikit-learn | 2 |
| After: sql | 2 |
| After: langchain | 2 |
| After: Git | 2 |
| After: feature extraction | 2 |
| After: predictive models | 2 |
| After: Natural Language Processing | 2 |
Roles most likely to require model deployment
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| ML Platform Engineer | 14 | 28.0% |
| Field Application Engineer | 4 | 18.2% |
| Applied Value Engineer | 5 | 17.2% |
| ML Ops Engineer | 5 | 14.7% |
| AI Manager | 3 | 14.3% |
| Back-End Engineer | 5 | 10.2% |
| Machine Learning Engineering Manager | 6 | 9.8% |
| AI/ML Scientist | 2 | 9.1% |
| Sales Engineer | 2 | 8.0% |
| AI Engineering Director | 4 | 7.8% |
Roles with the most model deployment postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 181 | 16.7% |
| Machine Learning Engineer | 172 | 15.8% |
| Software Engineer | 101 | 9.3% |
| AI Engineer | 53 | 4.9% |
| ML Engineer | 53 | 4.9% |
| Solutions Architect | 17 | 1.6% |
| Forward Deployed Engineer | 16 | 1.5% |
| MLOps Engineer | 15 | 1.4% |
| ML Platform Engineer | 14 | 1.3% |
| AI/ML Engineer | 13 | 1.2% |
Top companies posting jobs requiring model deployment
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| 48 | |
| Bjakcareer | 17 |
| Mastercard | 16 |
| Stripe | 11 |
| Bosch | 10 |
| Accenture | 10 |
| Adyen | 9 |
| Elsevier | 9 |
| Mercury | 9 |
| Axial Search | 8 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for model deployment
| Name | Postings | Share |
|---|---|---|
| London | 46 | 4.2% |
| San Francisco | 40 | 3.7% |
| New York City | 32 | 2.9% |
| Bengaluru | 24 | 2.2% |
| San Jose | 18 | 1.7% |
| Amsterdam | 15 | 1.4% |
| Hyderabad | 15 | 1.4% |
| Toronto | 15 | 1.4% |
| Singapore | 13 | 1.2% |
Skills commonly paired with model deployment
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-11-04. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning model deployment by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s model deployment postings by all model deployment postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.5% to 0.5%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,472 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 59c33744dec18866
- data_as_of
- 2026-11-04
- window_days
- 90