Machine Learning Engineer

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 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.

Exp : 3 Years - 5 Years

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/GCP 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).