Key Responsibilities

1. Model Development and Optimization:
   • Design and implement machine learning models for various use cases, such as recommendation systems, fraud detection, and predictive analytics.
   • Continuously refine and optimize algorithms to enhance accuracy and performance.
2. Data Collection and Preparation:
   • Collaborate with data engineers to ensure data quality, completeness, and suitability for model training.
   • Implement data preprocessing steps, such as normalization, feature engineering, and anomaly detection.
3. Deployment and Integration:
   • Work with software engineers to deploy models in production environments, ensuring scalability and efficiency.
   • Use tools like Docker and Kubernetes to containerize models for cloud deployment.
4. Monitoring and Maintenance:
   • Set up monitoring for model performance and ensure timely retraining and updating as necessary.
   • Troubleshoot and resolve any model performance issues post-deployment.
5. Collaboration and Innovation:
   • Collaborate with cross-functional teams to identify new AI/ML opportunities.
   • Stay updated on the latest machine learning trends, tools, and techniques to improve model outcomes.

Required Qualifications

• Experience: 4+ years in machine learning engineering or data science.
• Technical Skills:
  • Languages: Proficiency in Python, R, or Julia for machine learning.
  • Libraries and Frameworks: Strong knowledge of TensorFlow, PyTorch, Scikit-Learn, and data manipulation libraries like Pandas and Numpy.
  • Cloud Deployment: Familiarity with deploying ML models on AWS, Azure, or GCP.
  • Data Engineering Skills: Understanding of ETL processes and SQL for data extraction and manipulation.

Preferred Qualifications

• Master’s degree in Data Science, Statistics, or a related field.
• Experience in NLP, computer vision, or reinforcement learning.
• Certification in AI/ML.

Benefits

• Compensation: Competitive salary with performance bonuses.
• Health and Wellness: Comprehensive health insurance.
• Learning Opportunities: Access to workshops, online courses, and conferences.
• Flexible Work Environment: Remote options and flexible hours.