Company Overview :

Elgebra is a leading technology company specializing in developing cutting-edge AI-powered solutions for the financial services industry. We leverage machine learning and data science to create innovative products that enhance trading strategies, risk management, and customer experience. Our solutions are deployed across various platforms, impacting millions of users globally.

Role Overview :

As an ML Engineer at Elgebra, you will be responsible for designing, developing, and deploying machine learning models that drive critical business decisions. You will collaborate closely with data scientists, software engineers, and product managers to translate research prototypes into scalable and robust production systems. Your work will directly impact the performance of our AI-driven financial products and contribute to the company's overall success.

Key Responsibilities :

- Develop and deploy machine learning models for various financial applications, including fraud detection, algorithmic trading, and credit risk assessment, ensuring high accuracy and efficiency.

- Collaborate with data scientists to implement and optimize machine learning algorithms, contributing to improved model performance and faster iteration cycles.

- Build and maintain scalable data pipelines for model training and evaluation, ensuring data quality and availability for machine learning projects.

- Design and implement APIs for accessing machine learning models, enabling seamless integration with other Elgebra systems and applications.

- Monitor and troubleshoot machine learning models in production, proactively identifying and resolving issues to maintain optimal performance.

- Contribute to the development of best practices for machine learning engineering, promoting code quality, reproducibility, and maintainability.

Required Skillset :

- Demonstrated ability to develop and deploy machine learning models using Python and relevant libraries such as scikit-learn, TensorFlow, and PyTorch.

- Proven experience in applying deep learning techniques to solve complex problems in areas such as NLP and time series analysis.

- Strong understanding of data science principles, including data preprocessing, feature engineering, and model evaluation.

- Proficiency in using cloud platforms like Azure for machine learning model deployment and management.

- Excellent communication and collaboration skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences.

- Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.

- Ability to work effectively in a fast-paced, agile environment and adapt to changing priorities.