ATS Resume Keywords for AI/ML Engineer: Complete Keyword Guide
Landing a AI/ML Engineer role requires a resume loaded with the right keywords that ATS systems scan for. This guide provides a comprehensive list of ATS keywords categorized by hard skills, tools, soft skills, and certifications specifically for AI/ML Engineer positions. Using these keywords strategically will significantly improve your ATS score and interview callback rate.
Hard Skills Keywords for AI/ML Engineer
Hard skills are the most heavily weighted keywords in ATS scoring for AI/ML Engineer roles. These represent your core technical competencies and are typically listed as required or preferred qualifications in job descriptions.
Include these keywords in both your skills section and within your work experience bullet points. Contextual usage (showing the skill in action) is more powerful than just listing it. For example, rather than just listing 'Machine Learning,' write 'Built Machine Learning-based application that...'
- Machine Learning
- Deep Learning
- Natural Language Processing
- Computer Vision
- MLOps
- Model Training
- Feature Engineering
- Model Deployment
- Python
- TensorFlow
- PyTorch
- Data Pipeline
- Reinforcement Learning
Tools and Technologies for AI/ML Engineer
ATS systems specifically look for tool and technology keywords because they indicate hands-on, practical experience. For AI/ML Engineer positions, familiarity with industry-standard tools is often a hard requirement.
List these tools in your skills section with specificity. Instead of just 'cloud computing,' list specific platforms like 'TensorFlow' and 'PyTorch.' Many recruiters search the ATS database using specific tool names, so including them makes you discoverable.
- TensorFlow
- PyTorch
- Hugging Face
- MLflow
- Kubeflow
- AWS SageMaker
- Vertex AI
- Docker
- Spark
- Jupyter
- CUDA
- W&B
Soft Skills Keywords for AI/ML Engineer
While hard skills dominate ATS scoring for AI/ML Engineer roles, soft skills keywords are increasingly weighted by modern ATS systems. Many job descriptions specifically mention soft skills, and including them can differentiate you from candidates with similar technical profiles.
Don't just list soft skills—demonstrate them in your experience bullets. Instead of listing 'Research orientation,' write an achievement that shows this skill in action.
- Research orientation
- Problem-solving
- Communication
- Cross-functional collaboration
- Experimentation mindset
Certifications That Boost AI/ML Engineer ATS Scores
Professional certifications are high-value keywords for AI/ML Engineer positions. Many ATS configurations use certifications as screening criteria or score boosters. Including relevant certifications can add 5-10% to your overall ATS score.
List certifications with both the full name and abbreviation to catch both search patterns. Include the issuing organization and year obtained for completeness.
- AWS Machine Learning Specialty
- Google Cloud ML Engineer
- TensorFlow Developer Certificate
- Deep Learning Specialization (Coursera)
Example ATS-Optimized Bullets for AI/ML Engineer
Here are example achievement statements that effectively integrate multiple AI/ML Engineer keywords while demonstrating quantified impact. Each bullet contains 2-4 relevant keywords in natural context.
Notice how each bullet starts with an action verb, includes specific technologies or skills, and quantifies the result. This format simultaneously satisfies ATS keyword matching and impresses human reviewers.
- Deployed production ML model serving 5M+ predictions daily with <50ms latency
- Built recommendation engine increasing user engagement by 35% and revenue by $2M annually
- Developed NLP system automating document classification with 96% accuracy, replacing manual process
Pro Tips
Include at least 80% of the hard skills mentioned in the specific job description for AI/ML Engineer roles
Use both the full name and abbreviation for technical terms (e.g., 'Natural Language Processing (NLP)')
Place your strongest AI/ML Engineer keywords in your professional summary, skills section, and most recent job description
Mirror the exact phrasing from the job posting—if they say 'stakeholder management,' use that phrase
Include 2-3 certifications relevant to AI/ML Engineer to boost your ATS score by 5-10%
Common Mistakes to Avoid
Listing generic skills instead of specific AI/ML Engineer technologies and tools
Including keywords you can't actually demonstrate in an interview
Focusing only on hard skills and ignoring soft skills that AI/ML Engineer job descriptions emphasize
Using outdated technology keywords that don't match current job requirements
Not tailoring keywords to each specific job description