Machine Learning Engineer

Required Skills:

A bachelor's or master's degree in computer science, data science, information science or related field, or equivalent work experience.

• 5-7+ Years of relevant (Machine Learning) experience in the field.

AI/ML Expertise:

• Strong understanding of classical machine learning techniques such as: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting (e.g., XGBoost, LightGBM), K-Means, and SVM.
• Experience in statistical modeling, including methods like Bayesian inference, hypothesis testing, and time series forecasting.
• Familiarity with optimization algorithms (e.g., Gradient Descent, Genetic Algorithms, Simulated Annealing).
• Knowledge of evaluation metrics for classification, regression, and clustering tasks (e.g., ROC-AUC, MSE, silhouette score).

Programming and Tools:

• Proficiency in programming languages such as Python (NumPy, Pandas, scikit-learn, statsmodels) or R.
• Experience with machine learning libraries and frameworks (e.g., scikit-learn, XGBoost, LightGBM).
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly).

Mathematical and Statistical Knowledge:

• Strong foundation in linear algebra, calculus, probability, and statistics.
• Familiarity with statistical tools for hypothesis testing and model validation.
  

Preferred Qualifications:

• Familiarity with deep learning frameworks such as TensorFlow or PyTorch (even if secondary).
• Understanding of natural language processing techniques using traditional AI (e.g., TF-IDF,
  Latent Dirichlet Allocation (LDA)).
• Knowledge of explainability tools like SHAP or LIME.
  Exposure to cloud platforms (e.g., AWS, Azure, or Google Cloud) for deploying ML pipelines.
  Experience with time series forecasting tools (e.g., ARIMA, Prophet).
• Data Engineering Skills:
  Proficiency in SQL for data querying and manipulation.
  Familiarity with big data tools like Spark or Hadoop is a plus.
  

Familiarity with one or more data-oriented workflow orchestration frameworks

(KubeFlow, Airflow, Argo, etc.)

• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Exposure to machine learning methodology and best practices