Machine Learning Developer

4 days ago

Lahore, Punjab, Pakistan beBee Careers Full time
Job Overview

This is a challenging role for an experienced Machine Learning Engineer who can design, build and deploy ML models for real-time and batch processing use cases.

The ideal candidate will have a strong understanding of supervised, unsupervised, and deep learning techniques and be proficient in Python and libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, or Keras.

About the Role

We are looking for a highly skilled Machine Learning Engineer to work on end-to-end Al projects: from data exploration and feature engineering to model deployment and monitoring.

The successful candidate will collaborate with software engineers, data scientists, and product teams to define Al requirements and deliver integrated solutions.

Key Responsibilities
  • Design, build, and deploy ML models for real-time and batch processing use cases.
  • Work on end-to-end Al projects: from data exploration and feature engineering to model deployment and monitoring.
  • Collaborate with software engineers, data scientists, and product teams to define Al requirements and deliver integrated solutions.
  • Optimize model performance and ensure reliability and scalability in production environments.
  • Conduct model validation, A/B testing, and continuous performance monitoring.
  • Develop and maintain data pipelines, model training scripts, and reusable ML components.
Requirements
  • Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or related field.
  • 3+ years of experience in machine learning or Al model development.
  • Proficient in Python and libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, or Keras.
  • Strong understanding of supervised, unsupervised, and deep learning techniques.
  • Experience in feature engineering, model evaluation metrics, and overfitting/underfitting prevention.
  • Familiar with end-to-end ML lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring.
  • Experience with cloud platforms such as AWS(SageMaker), Azure ML, or GCP Al Platform.
  • Good grasp of APls, containers (Docker), version control (Git), and CI/CD pipelines for ML workflows.

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