Machine Learning Engineer

Requirements

• Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field.
  
  
  
• 3+ years of hands-on experience in machine learning (ML) development.
  
• Proficiency in one or more key ML domains and frameworks, including:
  
• Supervised learning (e.g., classification, regression)
  
• Unsupervised learning (e.g., clustering, anomaly detection)
  
• Time-series analysis
  
• Natural Language Processing (NLP)
  
• Computer Vision (CV)
  
• Statistical modeling
  
• Demonstrated ability to select, implement, and adapt ML models to address real-world business problems.
  
• Practical experience delivering data science and ML solutions in production environments.
  
• Strong understanding of the ML lifecycle, including:
  
• Data cleaning and preprocessing
  
• Feature engineering
  
• Model training and optimization
  
• Performance evaluation and tuning
  
• Proficiency in Python and common data science libraries such as Pandas, NumPy, Scikit-learn, etc.
  

• Demonstrated expertise in deep learning.
  
• Experience working with cloud data platforms, such as Databricks, AWS, or similar.
  
• Familiarity with MLOps practices and tools for model deployment, monitoring, and automation.
  
• Hands-on experience with PySpark and the Databricks platform.
  
• Passion for continuous learning; stays current with the latest developments in ML and AI.
  
• Bonus: Participation in Kaggle competitions and familiarity with industry benchmarks such as MLEBench.
  

Benefits