SENIOR MACHINE LEARNING ENGINEER

Senior Machine Learning Engineer - 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. This role requires a high degree of collaboration with key managers, product owners, and other peers and cross-functional partners that rely, produce, and interact with the data domain across the organization (e. g. , Data Engineers, Data Analysts, Backend Engineers, Marketing, Sales). The ML Engineer will focus on creating interpretable, accurate, and scalable predictive models utilizing datasets generated from our AWS and Snowflake environments. Additionally, you will translate model insights into actionable strategies that drive business decisions and financial inclusion. In addition to the technical responsibilities, you will coach and mentor your 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. You will exhibit and foster our culture and operating principles. Core Competencies Superb programming, software and data development skills You can independently devise and implement solutions to problems with minimal explanation needed. Strong communication skills You can efficiently translate between technical and non-technical audiences and have strong writing skills. High standards Your work is of the highest quality and you continue to raise the bar within your immediate team and our organization. Balance velocity with long-term goals You balance thinking big with delivering the right thing in an agile and speedy manner. You are curious, flexible, and nimble in your approach and implementation. Heart of a teacher You are a capable mentor and able to inspire and empower others on your team. Getting work done and driving excellence You strive for excellence and prioritize delivering high-quality outcomes and projects. . Adaptability You thrive in an environment that changes quickly and is constantly presenting new challenges. Required Qualifications Knowledge & Background: Masters degree, or Bachelors degree with 10+ years of relevant work experience, in computer science, mathematics, statistics, economics, or other quantitative disciplines with a focus on data analysis. AI & ML Experience: Very strong experience in AI and ML in the financial technology or service industry, including working with credit and financial datasets. AI & ML Engineering: Proven track record in building and deploying machine learning models, with a strong understanding of the theory and tradeoffs behind these techniques. AI Modeling: Proficiency in statistical and machine learning techniques for predictive modeling, classification, and regression. Programming Languages: Very strong knowledge of Python and SQL. Cloud Expertise: 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. AI & ML Platforms: 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. DataOp & MLOps: Experience implementing DataOps, MLOps, and/or DevSecOps in the AI, ML, and software development lifecycle. ML Languages: Experience building ML models with PyTorch, Scikit-learn, and GenAI models. LLM Models: Experience working with LLM frameworks such as HuggingFace libraries and with agent-based frameworks such as LangChain and Mirascope. Data Experience:Familiarity with Snowflake for cloud data warehousing, data integration, and efficient handling of large-scale data storage and processing. Preferred Qualifications Specialized Knowledge & Background: PhD degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis. FinTech/PropTech Expertise: Experience working in FinTech or PropTech sectors. Startup Experience: Familiarity with the dynamics of startup environments. Software Engineering Experience: Experience with microservices. Compliance Knowledge: Experience working in SOC2-certified, ISO-certified, or similar organizations. Key Technologies We Use Backend: Go, Python. Cloud: Amazon Web Services (AWS). Data: MongoDB, PostgreSQL, S3, Sagemaker, Snowflake, Tableau. Front End: React, Typescript. Mobile: Flutter, Dart.