AI/ML Specialist resume skills and keywords
In 132 AI/ML Specialist job postings from 90 employers analysed on October 23, 2026, the most requested skills are Python (76 %), LLMs (61 %), AWS (54 %), Machine learning (53 %) and Communication (51 %). Of the top 40 skills, 35 are hard skills, tools or certifications and 5 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 AI/ML Specialist job postings
| Skill | Share of postings | Type |
|---|---|---|
| Python | 76 % | Tool |
| LLMs | 61 % | Tool |
| AWS | 54 % | Tool |
| Machine learning | 53 % | Hard skill |
| Communication | 51 % | Soft skill |
| PyTorch | 42 % | Tool |
| Azure | 41 % | Tool |
| APIs | 39 % | Hard skill |
| Collaboration | 37 % | Soft skill |
| GCP | 36 % | Tool |
| RAG | 35 % | Tool |
| Generative AI | 35 % | Tool |
| TensorFlow | 34 % | Tool |
| Data Science | 31 % | Hard skill |
| Natural language processing | 28 % | Hard skill |
| Mentoring | 27 % | Soft skill |
| Artificial Intelligence | 27 % | Hard skill |
| Machine Learning Operations | 25 % | Hard skill |
| CI/CD | 24 % | Tool |
| Deep learning | 24 % | Hard skill |
| Problem Solving | 23 % | Soft skill |
| Experimentation | 21 % | Hard skill |
| Docker | 20 % | Tool |
| Leadership | 20 % | Soft skill |
| Machine Learning Models | 20 % | Hard skill |
| Algorithms | 20 % | Hard skill |
| Prompt engineering | 19 % | Hard skill |
| Kubernetes | 19 % | Tool |
| SQL | 19 % | Tool |
| Scikit-learn | 18 % | Tool |
| LangChain | 18 % | Tool |
| AI Tools | 17 % | Tool |
| Data pipelines | 17 % | Hard skill |
| Cloud infrastructure | 16 % | Hard skill |
| Guardrails | 14 % | Hard skill |
| Hugging Face | 14 % | Tool |
| Evaluation frameworks | 14 % | Tool |
| Git | 13 % | Tool |
| Responsible-AI | 13 % | Hard skill |
| Apache Spark | 13 % | Tool |
Hard skills vs soft skills vs tools and certifications
Hard skills (15)
- Machine learning 53 %
- APIs 39 %
- Data Science 31 %
- Natural language processing 28 %
- Artificial Intelligence 27 %
- Machine Learning Operations 25 %
- Deep learning 24 %
- Experimentation 21 %
- Machine Learning Models 20 %
- Algorithms 20 %
- Prompt engineering 19 %
- Data pipelines 17 %
- Cloud infrastructure 16 %
- Guardrails 14 %
- Responsible-AI 13 %
Soft skills (5)
- Communication 51 %
- Collaboration 37 %
- Mentoring 27 %
- Problem Solving 23 %
- Leadership 20 %
Tools and software (20)
- Python 76 %
- LLMs 61 %
- AWS 54 %
- PyTorch 42 %
- Azure 41 %
- GCP 36 %
- RAG 35 %
- Generative AI 35 %
- TensorFlow 34 %
- CI/CD 24 %
- Docker 20 %
- Kubernetes 19 %
- SQL 19 %
- Scikit-learn 18 %
- LangChain 18 %
- AI Tools 17 %
- Hugging Face 14 %
- Evaluation frameworks 14 %
- Git 13 %
- Apache Spark 13 %
Keyword phrases recruiters use
Multi-word phrases that recur across these ai/ml specialist 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 AI/ML Specialist resume example. Then prepare for the AI/ML Specialist interview questions that test them.
Build a ai/ml specialist resume with these skills
Start in the editor, tailor your bullets to the job you want and download an ATS-friendly resume.