Machine Learning Engineer - Python/Hadoop

Machine Learning Engineer

Key Responsibilities

• Data Analysis & Feature Engineering

  : Conduct comprehensive

  feature engineering, data analysis, and data exploration

  to extract valuable insights from diverse datasets.

• Model Development & 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 (ANN), Convolutional Neural Networks (CNN), 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 & Monitoring

  : Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on real-world feedback and performance metrics.

• Cross-Functional Collaboration

  : Collaborate seamlessly with cross-functional teams, including data scientists, software engineers, and product managers, to align ML solutions with overarching business goals.

• Statistical & Mathematical Application

  : Utilize fundamental statistical knowledge and strong mathematical principles to ensure the reliability, validity, and interpretability of models.

• Innovation & Research

  : Stay abreast of the latest advancements in ML and AI, actively bringing in new techniques and research findings to drive innovation within the team and company.

Required Skills

• 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.
• Ability to optimize models for high accuracy and performance in real-world scenarios.

Preferred Skills

• Experience with

  Big Data technologies (Hadoop, Spark, etc.)

  .
• 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.