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 current 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).

📌 Senior Software Engineer I - ML (Bengaluru)
🏢 Rakuten International
📍 Bengaluru