Machine Learning Engineer

Job Description: We are seeking an enthusiastic Machine Learning Engineer to enhance our AI-driven lending portfolio. The successful candidate will be instrumental in developing, optimizing, and maintaining machine learning models that underpin our wallet and loan services, utilizing the latest in technology and methodologies to ensure high performance and reliability.

Key Responsibilities:

• Develop, train, and deploy machine learning models using frameworks like scikit-learn, TensorFlow, PyTorch, Caret and Tidymodels, incorporating both Python and R environments.
• Implement and optimize data processing workflows using Pandas, Dask, and Tidyverse, ensuring efficient handling of data within RAM constraints using chunking strategies.
• Support the development and enhancement of AI-powered chatbots for customer interaction and support.
• Experience with Google Cloud Services, particularly Google Cloud Run and Cloud Build, for deploying and managing scalable applications.
• Proficiency in version control systems, including working with Git repositories for collaborative code development and management.
• Utilize Flask and Plumber to develop and deploy APIs for model hosting, ensuring scalability and reliability within Dockerized environments.
• Monitor model performance and data integrity to detect and address data drift and concept drift, maintaining high model accuracy and reliability.
• Collaborate with DBAs to integrate machine learning models seamlessly with existing Oracle and MySQL/Postgresql databases.
• Support the deployment and maintenance of the MLOps infrastructure using MLflow, managing the lifecycle of machine learning models from development to production.
• Generate insights and visualizations using Prometheus, Grafana, and the ELK stack to monitor system performance and user interactions.
• Continuously research and implement best practices in machine learning and data science to enhance product features and operational efficiency.

Qualifications:

• Master’s / Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Economics, Business or a related field.
• 2 to 4 years of experience in a machine learning engineering role.
• Strong proficiency in Python or R, with practical knowledge of SQL and NoSQL databases.
• Experience with machine learning libraries and frameworks, and familiarity with Docker and container management.
• Knowledge of data processing frameworks and tools, including Dask and Pandas.
• Proven ability to develop and manage RESTful APIs using Flask or Plumber.
• Experience with monitoring tools such as Evidently AI, and familiarity with Prometheus, Grafana, and ELK Stack for operational monitoring.

Preferred Skills:

• Practical exposure to financial services or fintech, particularly in areas of credit scoring or fraud detection.
• Experience with Google Cloud Services is preferred.
• Experience with DevOps practices and tools.
• Experience with Oracle and ArangoDB in a production environment.