Automation index
How much of a DevOps engineer’s job can AI already do?
Today an AI can already do 37% of this job. Architecture, the hard calls, and review still need someone who has broken production.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
DevOps engineer
37%
AI can already do 37% of this job.
#12 / 623
View rankingDevOps engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance.EstimateNo public source provides a precise figure. This is our estimate. | 11% | 28% | +3.1 |
Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcomes and consequences of design.EstimateNo public source provides a precise figure. This is our estimate. | 10% | 48% | +4.8 |
Store, retrieve, and manipulate data for analysis of system capabilities and requirements.EstimateNo public source provides a precise figure. This is our estimate. | 10% | 48% | +4.8 |
Confer with systems analysts, engineers, programmers and others to design systems and to obtain information on project limitations and capabilities, performance requirements and interfaces.EstimateNo public source provides a precise figure. This is our estimate. | 9% | 48% | +4.3 |
Supervise the work of programmers, technologists and technicians and other engineering and scientific personnel.EstimateNo public source provides a precise figure. This is our estimate. | 8% | 22% | +1.8 |
Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 48% | +3.4 |
Develop or direct software system testing or validation procedures, programming, or documentation.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 55% | +3.9 |
Consult with customers or other departments on project status, proposals, or technical issues, such as software system design or maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 5% | 28% | +1.4 |
Other less frequent tasks (weight estimated from frequency).EstimateNo public source provides a precise figure. This is our estimate. | 33% | 28% | +9.2 |
| Total | 100% | 37% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- write code
- programming
- software development
- coding
- debug code
- implement cloud security and compliance
- systems development life-cycle
- cloud security and compliance
- computer programming
- ICT system integration
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511