Remote Staff AI/ML Applied Engineer

Description:

• Zipdev is seeking a ML/AI Applied Engineer to apply advanced machine learning models that predict and model creditworthiness, transaction labeling, identity mapping, underwriting, cashflow, lease renewals, and other key financial factors.
• This role will work closely with the Data Analytics and Data Engineering teams to ensure the models are trained on high-quality data and integrated into production systems.
• The position requires a high degree of collaboration with key managers, product owners, and other peers and cross-functional partners that rely on, produce, and interact with the data domain across the organization.
• The ML Engineer will focus on creating interpretable, accurate, and scalable predictive models utilizing datasets generated from AWS and Snowflake environments.
• You will translate model insights into actionable strategies that drive business decisions and financial inclusion.
• In addition to technical responsibilities, you will coach and mentor fellow data analysts and data engineers.
• You will ensure efficient delivery through effective planning, engaging with others, prioritizing, and developing, testing, and releasing your work.

Requirements:

• A Master’s degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis is required.
• A relevant bachelor’s degree in a STEM field with 10+ years of relevant work experience in Machine Learning and Statistics is necessary.
• Strong experience in AI and ML in the financial technology or service industry, including working with credit and financial datasets, is essential.
• Experience implementing DataOps, MLOps, and/or DevSecOps in the AI, ML, and software development lifecycle is required.
• Experience building ML models with PyTorch, Scikit-learn, and GenAI models is necessary.
• Very strong knowledge of Python and SQL is required.
• Strong experience with AWS cloud services and tools, including AWS SageMaker for model development, training, and deployment, and AWS Bedrock for building and fine-tuning foundation models, is essential.
• A proven track record in building and deploying machine learning models, with a strong understanding of the theory and tradeoffs behind these techniques, is required.
• Proficiency in statistical and machine learning techniques for predictive modeling, classification, and regression is necessary.
• Experience in working with model registry tools such as MLflow, SageMaker Model Registry, or other similar systems to track, version, and manage machine learning models throughout their lifecycle is required.
• Experience working with LLM frameworks such as HuggingFace libraries and with agent-based frameworks such as LangChain and Mirascope is necessary.
• Familiarity with Snowflake for cloud data warehousing, data integration, and efficient handling of large-scale data storage and processing is required.

Benefits:

• Work remotely Monday - Friday, 40 hours a week (no weekends).
• Vacation: 10 business days a year.
• Holidays: 5 National Holidays a year.
• Company Holidays: 5 Company Holidays a year (Christmas Eve, Christmas Day, New Year's Eve, New Year's Day, Zipdev Day).
• Parental Leave is offered.
• Health Care Reimbursement is provided.
• Active Lifestyle Reimbursement is available.
• Quarterly Home Office Reimbursement is included.
• Payroll Deduction Purchase Plans are offered.
• Longevity Bonus is available.
• Continuous Learning Bonus is provided.
• Access to Training and Professional Development Platforms is included.
• The position is fully remote.