MLOps Engineer resume skills and keywords
In 82 MLOps Engineer job postings from 71 employers analysed on October 23, 2026, the most requested skills are Python (83 %), CI/CD (71 %), Kubernetes (65 %), Machine Learning Operations (63 %) and AWS (61 %). Of the top 40 skills, 36 are hard skills, tools or certifications and 4 are soft skills.
Download the full list as a CSV or check your own resume against a posting with the free keyword scanner.
Top 40 skills in MLOps Engineer job postings
| Skill | Share of postings | Type |
|---|---|---|
| Python | 83 % | Tool |
| CI/CD | 71 % | Tool |
| Kubernetes | 65 % | Tool |
| Machine Learning Operations | 63 % | Hard skill |
| AWS | 61 % | Tool |
| Machine learning | 50 % | Hard skill |
| Infrastructure as code | 46 % | Hard skill |
| Docker | 45 % | Tool |
| Terraform | 45 % | Tool |
| MLflow | 44 % | Hard skill |
| Communication | 40 % | Soft skill |
| Versioning | 39 % | Hard skill |
| LLMs | 35 % | Tool |
| GCP | 35 % | Tool |
| Experiment tracking | 32 % | Hard skill |
| PyTorch | 30 % | Tool |
| Collaboration | 30 % | Soft skill |
| Model serving | 30 % | Hard skill |
| Cloud infrastructure | 29 % | Hard skill |
| Data pipelines | 28 % | Hard skill |
| Azure | 28 % | Tool |
| Cloud Platforms | 27 % | Tool |
| GitHub Actions | 24 % | Tool |
| Distributed systems | 24 % | Hard skill |
| Model Deployment | 24 % | Hard skill |
| Data Science | 24 % | Hard skill |
| APIs | 23 % | Hard skill |
| Problem Solving | 23 % | Soft skill |
| Alerting | 22 % | Hard skill |
| Mentoring | 22 % | Soft skill |
| Apache Spark | 21 % | Tool |
| Distributed training | 21 % | Hard skill |
| Experimentation | 20 % | Hard skill |
| Data Engineering | 20 % | Hard skill |
| Airflow | 18 % | Tool |
| Debugging | 18 % | Hard skill |
| Robotics | 17 % | Hard skill |
| System design | 17 % | Hard skill |
| Go | 16 % | Tool |
| Lineage | 16 % | Hard skill |
Hard skills vs soft skills vs tools and certifications
Hard skills (21)
- Machine Learning Operations 63 %
- Machine learning 50 %
- Infrastructure as code 46 %
- MLflow 44 %
- Versioning 39 %
- Experiment tracking 32 %
- Model serving 30 %
- Cloud infrastructure 29 %
- Data pipelines 28 %
- Distributed systems 24 %
- Model Deployment 24 %
- Data Science 24 %
- APIs 23 %
- Alerting 22 %
- Distributed training 21 %
- Experimentation 20 %
- Data Engineering 20 %
- Debugging 18 %
- Robotics 17 %
- System design 17 %
- Lineage 16 %
Soft skills (4)
- Communication 40 %
- Collaboration 30 %
- Problem Solving 23 %
- Mentoring 22 %
Tools and software (15)
- Python 83 %
- CI/CD 71 %
- Kubernetes 65 %
- AWS 61 %
- Docker 45 %
- Terraform 45 %
- LLMs 35 %
- GCP 35 %
- PyTorch 30 %
- Azure 28 %
- Cloud Platforms 27 %
- GitHub Actions 24 %
- Apache Spark 21 %
- Airflow 18 %
- Go 16 %
Keyword phrases recruiters use
Multi-word phrases that recur across these mlops engineer postings, with the share of postings that use each one.
How to use this on your resume
Treat the table as a checklist, not a script. Put the skills you genuinely have in your skills section, using the same wording as the posting, and back the top ones with a bullet that shows the result.
Each job posting differs from the average, so compare your resume with the actual ad: paste it into the resume keyword scanner to see which terms you already have and which are missing. The ATS keywords guide explains where to place them so applicant tracking systems read them.
Skip any skill you cannot talk about in an interview; a keyword that falls apart under one question costs more than it gains. For a full layout with these skills in context, see the MLOps Engineer resume example. Then prepare for the MLOps Engineer interview questions that test them.
Build a mlops engineer resume with these skills
Start in the editor, tailor your bullets to the job you want and download an ATS-friendly resume.