AI and Machine Learning Engineer

About you :

Bachelor's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline. Master's degree is desirable.

Typically, - years' experience.

A solid understanding of mathematics, including linear algebra, calculus, and probability theory, is essential for working with machine learning algorithms. Additionally, a good grasp of statistical concepts and methodologies is necessary for model evaluation and analysis.

Proficiency in programming languages such as Python, R, or Java is expected. Knowledge of relevant libraries and frameworks like TensorFlow, PyTorch, scikit-learn, or Keras is highly beneficial. Experience with SQL for data manipulation and database querying may also be necessary.

Understanding of GitHub CoPilot, Cursor, NN, vibe coding, Windsurf, and similar technologies

Experience in Cloud Infrastructure (AWS, Azure, etc)

Knowledge of Open Source, Linux, etc

Understanding of Devops, SRE

Hands-on experience in developing and implementing machine learning models, including through internships, research projects, or previous job roles where you worked on machine learning initiatives.

Practical experience with data cleaning, data pre-processing techniques, and feature engineering is important.

Experience designing and developing machine learning models using algorithms such as linear regression, deciding trees, random forests, support vector machines, or deep learning models is crucial. Familiarity with model evaluation techniques, hyperparameter tuning, and cross-validation is also expected.

Proficiency in software engineering principles and practices is valuable. Experience with version control systems (, Git), software development methodologies, and deploying machine learning models in production environments is advantageous.

Strong communication skills, both technical and non-technical, are important for collaborating with team members, explaining complex concepts, and presenting findings to stakeholders. The ability to work in cross-functional teams and adapt to evolving project requirements is highly valued.