Why Skills Section is Important?
- Shows recruiters your core strengths in one glance.
- Increases chances of your resume appearing in job portal searches.
- Helps ATS software select your resume when recruiters search for keywords like Python, Generative AI, LLM, LangChain.
- Tells HR if you are a fresher, mid-level, or senior engineer.
How to List Your Skills
- Use bullet points instead of long sentences.
- Divide skills into categories for easy reading.
- Always include keywords from the job description.
- Keep the list updated with latest Generative AI tools.
Example Skill Categories for Generative AI Engineer Resume
Programming & Frameworks
- Python, R, Java
- TensorFlow, PyTorch, Keras
Generative AI & NLP
- Large Language Models (GPT, LLaMA, Falcon)
- LangChain, Hugging Face Transformers
- Text-to-Image Models (Stable Diffusion, MidJourney, DALL·E)
- NLP (Named Entity Recognition, Sentiment Analysis, Chatbots)
Data Engineering & Databases
- SQL, Snowflake, Pandas, NumPy
- Data Preprocessing & Feature Engineering
Cloud & Deployment
- AWS, Azure, Google Cloud
- Docker, Kubernetes, MLOps
Tools & Extra Skills
- Git, GitHub, Streamlit, FastAPI
- Synthetic Data Generation
Soft Skills (often ignored but important)
- Problem-Solving
- Teamwork & Collaboration
- Communication Skills
- Project Management
Best Hard Skills to Feature in Your Generative AI Engineer Resume
What are Hard Skills?
- Hard skills are technical, measurable, and job-specific skills.
- Example: Python programming, TensorFlow, GPT model training.
- These can be learned through training, projects, or certifications.
Best Hard Skills for Generative AI Engineer Resume
Programming Languages
- Python (must-have for all AI engineers)
- R (for data analysis and ML)
- Java / C++ (for scalable AI systems)
Machine Learning & Deep Learning Frameworks
- TensorFlow, PyTorch, Keras
- Scikit-learn, OpenAI API
- Hugging Face Transformers
Generative AI Tools & Models
- Large Language Models (GPT, LLaMA, Falcon)
- LangChain (for building AI pipelines)
- Stable Diffusion, MidJourney, DALL·E (for text-to-image)
- Synthetic Data Generation tools
Natural Language Processing (NLP)
- Text classification, Sentiment analysis
- Named Entity Recognition (NER)
- Building AI Chatbots
- Prompt Engineering
Data Engineering & Databases
- SQL, Snowflake, MongoDB
- Pandas, NumPy, Data Wrangling
- Feature Engineering
Cloud Platforms & Deployment
- AWS (SageMaker, Bedrock)
- Google Cloud AI, Vertex AI
- Microsoft Azure AI Services
- Docker, Kubernetes, MLOps pipelines
Extra Technical Tools
- Git, GitHub (version control)
- Streamlit, Flask, FastAPI (AI apps)
- APIs & Microservices