Senior Software Engineer I - ML

Job Description:

Responsibilities:

• Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders;
• Develop scalable algorithms and methods to provide real-time recommendations;
• Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver;
• Work with engineering teams to implement new models while considering functionality for recommendation output;
• Collaborate with Data Scientists to prototype new algorithms and design experiments for evaluation to enhance our marketing efficiency;
• Contribute to a culture of continuous improvement, and data driven results;
• Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models;
• Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models;
• Discover data sources, ingest and cleanse them in a meaningful way for data processing.
• Participate in design discussions about new features and approaches to implementing new services;
• Take end to end ownership of Machine Learning systems – from data pipelines and training, to real-time prediction engines;
• Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.

Requirements:

• Bachelor’s degree or higher (completed and verified prior to start) from an accredited university;
• Preferably working experience in FinTech or Credit Card industry.
• Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark);
• Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code;
• Should have clear understanding of Generative AI and various LLM models.
• Databricks Delta Lake architecture and MLflow is an added advantage.
• Good knowledge of Azure cloud infrastructure.
• Strong knowledge of machine learning techniques (i.e. classification, regression, and clustering);
• BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience;
• Understand machine learning principles (training, validation, etc.);
• Expertise in visualizing and manipulating big datasets
• Knowledge of data query and data processing tools (i.e. SQL).