About the job

Are you passionate about reshaping the future of finance using cutting-edge AI technology?

FINQ is a world innovator, crafting the next generation of financial services, and it’s financed by 2 of the leading successful innovators – Nir Zuk and Eldad Tamir.

Role Description

We are seeking a talented and experienced Machine Learning Engineer to join our team. As a Machine Learning Engineer, you will play a crucial role in developing, implementing, and maintaining machine learning models and systems to solve complex problems and enhance our products and services.

Responsibilities:

· Model Development: Design, develop, and implement machine learning models and algorithms to address specific business needs and challenges.

· Data Collection and Preprocessing: Collect, clean, and preprocess large datasets to ensure data quality and suitability for training machine learning models.

· Feature Engineering: Identify relevant features and engineering techniques to enhance model performance and predictive accuracy.

· Model Training and Evaluation: Train, validate, and evaluate machine learning models using appropriate evaluation metrics and techniques.

· Model Deployment: Deploy machine learning models into production environments, ensuring scalability, reliability, and efficiency.

· Monitoring and Maintenance: Monitor model performance and health in production, diagnose and troubleshoot issues, and implement improvements as needed.

Requirements:

· Bachelor’s Degree in Computer Science, Statistics, Applied Mathematics, or other related fields.

· 5+ years of experience in developing end to end machine learning solutions, involving substantial software development (programming) efforts, 3+ years of hands-on experience with Python.

· Proven experience (2+ years) in developing and deploying machine learning models and systems in production environment.

· Solid experience with re-enforcement learning models

· Solid understanding of machine learning algorithms, techniques, and best practices, with expertise in areas such as supervised learning, unsupervised learning, and deep learning.

· Solid understanding of backend development principles, including software architecture, API design, databases, and server-side frameworks.

· Proficiency in data preprocessing techniques, exploratory data analysis and feature engineering.

· Experience with explaining ML using Explainable AI or using Surrogate Model

· Passion for technology and staying updated with current best practices.