Andersen is an international IT company and one of the leaders in outsourcing development. 90% of our projects are in banking and fintech for clients around the world. We are growing fast — and we want our engineers to grow just as quickly.
A traineeship is more than just learning. It’s the shortest path into the IT industry for those who are at the beginning of their career and don’t yet have commercial experience.
The ML Engineer Traineeship at Andersen is your entry point into real-world machine learning projects, with the opportunity for further employment. You will gain hands-on experience with modern ML tools and start your career in a team of professionals.
The program is open in Kazakhstan:
office attendance is required or an online format is available.
What we offer:
4 months: from your first day in the Lab to your first day on a commercial project;
Full-time — 5 days a week, 8 hours a day: an intensive format that truly accelerates your growth;
Bring your own laptop: your device + our training = a specialist ready for real tasks;
Focus on learning: this traineeship is primarily about gaining knowledge and skills. Successful graduates receive a Junior 1 offer;
Training contract for 1.5 years: you learn — we hire.
Who we are looking for:
The one who understands the value of machine learning technologies and wants to build a career in ML engineering.
Requirements:
Intermediate or higher level of English (verbal communication);
understanding of probability, statistics and hypothesis testing fundamentals;
data preprocessing and EDA skills (missing values, outliers, feature engineering);
knowledge of classical ML algorithms (linear/logistic regression, trees, ensemble methods, clustering);
model evaluation techniques (accuracy, precision, recall, ROC-AUC, regression metrics, cross-validation);
experience with scikit-learn and basic ML pipelines;
understanding of feature selection, scaling and dimensionality reduction methods;
basic knowledge of NLP concepts and text representations (TF-IDF, embeddings);
understanding of ML system lifecycle (training, evaluation, deployment basics).
Will be a plus:
experience with gradient boosting frameworks (XGBoost, LightGBM, CatBoost);
knowledge of model interpretability techniques (SHAP, LIME);
familiarity with deep learning basics (NN, CNN, transformers);
experience with ML tooling (MLflow, DVC, FastAPI, Docker);
understanding of model monitoring and data drift concepts.
What’s cool about us:
Apply now — we review every application.
Challenges are part of the journey — real opportunities are the reward.
Begin your Machine Learning career with Andersen!