AI/ML Model Developer

2 weeks ago

Kanpur, Uttar Pradesh, India beBeeMachineLearning Full time ₹ 1,04,000 - ₹ 13,08,780

We're seeking an experienced AI/ML professional to spearhead the development and implementation of advanced machine learning models.

Job Description

The ideal candidate will have a strong background in machine learning, deep learning, and artificial intelligence. They will be responsible for designing, developing, and deploying sophisticated ML models that drive business value and improve operational efficiency.

Key responsibilities include:

  • Data analysis and feature engineering: Conduct comprehensive data analysis and feature engineering to extract valuable insights from diverse datasets.
  • Model development and optimization: Develop and optimize cutting-edge machine learning models to achieve high accuracy, efficiency, and performance.
  • Deep learning implementation: Design and implement advanced deep learning models, including artificial neural networks, convolutional neural networks, and reinforcement learning techniques.
  • Imbalanced data handling: Effectively handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness, generalization, and robustness.
  • Model deployment and monitoring: Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on real-world feedback and performance metrics.
Requirements
  • Hands-on experience in machine learning and deep learning.
  • Strong expertise in feature engineering, data exploration, and data preprocessing.
  • Experience with imbalanced datasets and techniques to improve model generalization.
  • Proficiency in Python, TensorFlow, Scikit-learn, and other relevant ML frameworks and libraries.
  • Strong mathematical and statistical knowledge coupled with excellent problem-solving skills.
Preferred Qualifications
  • Experience with big data technologies (e.g., Hadoop, Spark).
  • Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
  • Experience in automating ML pipelines with MLOps practices.
  • Experience in model deployment using cloud platforms (e.g., AWS, GCP, Azure) or MLOps tools.

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