Professional roles in AI

Machine Learning Engineer

Role:
Builds, tests, and deploys machine learning models in production environments. Their focus is on turning AI research or prototypes into scalable, efficient, and reliable systems.

Knowledge and skills:

• Strong programming (Python, Java, C++).
• Proficiency in frameworks like TensorFlow, PyTorch, Scikit-learn.
• Knowledge of algorithms, data preprocessing, model optimization, and MLOps.
• Understanding of cloud services (AWS, GCP, Azure).

Typical profile:
A software engineer with solid knowledge of mathematics, statistics, and data pipelines.

Machine Learning Scientist

Role:
Focuses on creating new algorithms, improving existing ML methods, and conducting experimental research. They usually work in research-oriented teams rather than product deployment.

Knowledge and skills:

• Deep understanding of mathematics, statistics, and probability.
• Advanced ML theory (deep learning architectures, optimization methods).
• Often holds a PhD or research experience in AI.
• Skilled in Python, R, and research libraries.

Typical profile:
A researcher or scientist developing the next generation of ML models.

Software Engineer

Role:
Designs and builds software applications or systems. In AI contexts, they integrate AI models into products or develop infrastructure to support AI workloads.

Knowledge and skills:

• Strong programming fundamentals and design patterns.
• Experience with APIs, databases, and version control.
• Understanding of deployment, testing, and debugging.
• May work closely with ML engineers to integrate models into applications.

Typical profile:
A generalist engineer focused on building reliable, maintainable code.

Research engineer

Role:
Bridges the gap between research and implementation. They help transform theoretical ideas into working prototypes.

Knowledge and skills:

• Strong programming skills and applied ML experience.
• Good understanding of research papers and ability to reproduce results.
• Works with scientists to implement experiments and validate hypotheses.

Typical profile:
A mix of engineer and researcher, good at both coding and experimenting.

Robotics Scientist

Role:
Designs algorithms and systems that allow robots to perceive, plan, and act autonomously.

Knowledge and skills:

• Control theory, kinematics, motion planning, and sensor fusion.
• Machine learning, especially computer vision and reinforcement learning.
• Programming (Python, C++, ROS framework).
• Strong background in physics, mechanics, and AI for perception.

Typical profile:
A researcher or engineer specializing in autonomous systems or robotics labs.

Data Scientist

Role:
Analyzes data to extract insights, build predictive models, and guide decision-making.

Knowledge and skills:

• Statistics, data visualization, and exploratory data analysis.
• Proficiency in Python, R, SQL, and data tools (Pandas, NumPy, Power BI).
• Knowledge of machine learning for predictive analytics.
• Business understanding to translate data into actionable insights.

Typical profile:
An analytical thinker who connects data patterns with business strategy.

Business Intelligence (BI) developer

Role:
Builds data systems, dashboards, and reporting tools that support business decisions.

Knowledge and skills:

• Expertise in data warehouses, ETL processes, and databases.
• Tools like Power BI, Tableau, or Looker.
• SQL and sometimes Python or DAX.
• Focused more on reporting than machine learning.

Typical profile:
A data professional focused on visualization, reporting, and data-driven decision support.

Algorithm specialist

Role:
Designs and optimizes algorithms for specific problems, e.g., search, recommendation, optimization, or signal processing.

Knowledge and skills:

• Strong mathematical and algorithmic foundation.
• Knowledge of computational complexity and optimization techniques.
• Programming in languages like C++, Python, or Java.
• Sometimes works on low-level efficiency and scalability.

Typical profile:
A problem solver who focuses on the logic and performance of algorithms rather than large-scale systems.

Natural Language Processing engineer

Role:
Builds systems that understand, generate, or analyze human language (chatbots, translators, sentiment analysis, etc.).

Knowledge and skills:

• Deep learning for language models (Transformers, LLMs).
• Text preprocessing, tokenization, embeddings.
• Libraries like Hugging Face, spaCy, NLTK.
• Linguistic and semantic understanding.

Typical profile:
An AI engineer specialized in working with human language data.