Machine Learning Engineer

Project Details

• Design, develop, and deploy scalable machine learning models and systems to solve real-world problems across various domains.
• Optimize model performance through hyperparameter tuning, feature engineering, and algorithm selection.
• Build and maintain robust data pipelines and infrastructure to support model training and inference.
• Implement monitoring and logging solutions to track model performance, detect drift, and ensure reliability in production environments.
• Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into applications and services.
• Ensure models adhere to security, privacy, and compliance standards throughout the development lifecycle.
• Maintain detailed documentation for model architecture, training processes, and deployment workflows.
• Stay up-to-date with the latest advancements in machine learning, deep learning, and AI frameworks. Continuously explore opportunities to improve model accuracy and efficiency.

Job Experience

• 5+ years’ experience in machine learning engineering or related roles with a strong foundation in computer science and applied mathematics.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience with cloud-based ML services (e.g., SageMaker, Vertex AI, Azure ML) and containerization tools (Docker, Kubernetes).
• Strong understanding of data structures, algorithms, and software engineering principles.
• Familiarity with MLOps practices including CI/CD for ML, model versioning, and automated deployment.
• Experience working with large-scale datasets and distributed computing environments.
• Excellent problem-solving skills and ability to translate business requirements into technical solutions.
• Knowledge of ethical AI principles, model interpretability, and fairness in machine learning.