AI/ML Engineer

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 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

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Requiremen t

• s:Bachelor’s degree or higher (completed and verified prior to start) from an accredited universit
• y.Preferably working experience in FinTech or Credit Card industr
• y.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 cod
• e.Should have clear understanding of Generative AI and various LLM model
• s.Databricks Delta Lake architecture and MLflow is an added advantag
• e.Good knowledge of Azure/GCP cloud infrastructur
• e.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 experienc
• e.Understand machine learning principles (training, validation, etc
• .)Expertise in visualizing and manipulating big datase
• tsKnowledge of data query and data processing tools (i.e. SQL