Machine Learning Engineer

Roles & Responsibilities

• Model Development

  Design, develop, and implement machine learning algorithms and models to solve complex problems, ensuring robustness, scalability, and effectiveness across applications.

• Data Preprocessing

  Engage in data cleaning, transformation, and augmentation to ensure high-quality input for training models. Utilize feature engineering techniques to enhance data utility.

• Model Evaluation

  Perform rigorous evaluation of models using cross-validation and other statistical methods, ensuring they meet performance metrics like accuracy, recall, and precision.

• System Integration

  Integrate machine learning solutions into existing systems, facilitating seamless deployment and validation, ensuring efficient interfacing with other technology stacks.

• Performance Optimization

  Optimize model performance through hyperparameter tuning and distributed computing, achieving reduced computation time and resource utilization for scalable deployment.

• Stay Current with Trends

  Stay updated with the latest advancements in machine learning, applying cutting-edge techniques and technologies to enhance current projects and drive innovation.

• Code Maintenance and Documentation

  Maintain and update existing machine learning codebases, ensuring they are well-documented, scalable, and reusable for future development and team collaboration.

• Collaboration with Stakeholders

  Work collaboratively with cross-functional teams to understand business needs, translate them into technical requirements, and deliver impactful machine learning solutions.

Typical Required Skills and Qualifications

• 3+ years of experience in machine learning or data science

• Proficiency in programming languages such as Python, R, or Java

• Experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-Learn

• Familiarity with cloud services such as AWS, Google Cloud, or Azure