ML/AI Roles Explained

Core ML/AI Roles

ML Engineer

• Build and deploy ML models to production
• Focus: Engineering, scalability, MLOps
• Tech stack: Python, TensorFlow/PyTorch, Docker, Kubernetes
• Salary: $120K-$200K (Entry), $200K-$350K (Mid), $350K-$600K+ (Senior)

Data Scientist

• Analyze data, build models, extract insights
• Focus: Statistics, experimentation, business impact
• Tech stack: Python, R, SQL, Jupyter, scikit-learn
• Salary: $100K-$180K (Entry), $180K-$280K (Mid)

AI Engineer

• Build AI applications using LLMs and AI APIs
• Focus: Prompt engineering, RAG, AI agents
• Tech stack: LangChain, OpenAI API, vector databases
• Salary: $110K-$190K (Entry), $190K-$320K (Mid)

Research Scientist

• Advance state-of-the-art ML techniques
• Focus: Publishing papers, novel algorithms
• Requirements: PhD usually required
• Salary: $150K-$250K (Entry), $250K-$500K+ (Senior)

MLOps Engineer

• Infrastructure for ML model deployment
• Focus: CI/CD, monitoring, orchestration
• Tech stack: Kubernetes, MLflow, Airflow, DVC
• Salary: $130K-$210K (Entry), $210K-$380K (Mid)

How to Choose Your Path

ML Engineer if you love:

• Building production systems
• Coding and engineering challenges
• Seeing models deployed at scale

Data Scientist if you love:

• Exploratory data analysis
• Business problem solving
• Statistics and experimentation

AI Engineer if you love:

• Building AI applications quickly
• Working with LLMs and latest AI tools
• Product development

Research Scientist if you love:

• Theoretical work
• Publishing and academia
• Pushing boundaries of what's possible

:::