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Required Data Infrastructure Engineer
What Youll Do:
Design, implement, and enhance robust and scalable infrastructure that enables efficient deployment, monitoring, and management of machine learning models in production. In this role, you will bridge the gap between research and production environments, streamline data and feature pipelines, optimize model serving, and ensure governance and reproducibility across our ML lifecycle.
Responsibilities:
Decouple data prep from model training to accelerate experimentation and deployment
Build efficient data workflows with versioning, lineage, and optimized resource use (e.g., Snowflake, Dask, Airflow)
Develop reproducible training pipelines with MLflow, supporting GPU and distributed training
Automate and standardize model deployment with pre-deployment testing (E2E, dark mode)
Maintain a model repository with traceability, governance, and consistent metadata
Monitor model performance, detect drift, and trigger alerts across the ML lifecycle
Enable model comparison with A/B testing and continuous validation
Support infrastructure for deploying LLMs, embeddings, and advanced ML use cases
Manage a unified feature store with history, drift detection, and centralized feature/label tracking
Establish a single source of truth for features across research and production across research and production.

Required Data Engineer
As part of our Data Engineering team, you will not only build scalable data platforms but also directly enable portfolio growth by supporting new funding capabilities, loan sales and securitization, and improving cost efficiency through automated and trusted data flows that evolve our accounting processes.
Responsibilities:
Design and build data solutions that support our core business goals, from enabling capital market transactions (loan sales and securitization) to providing reliable insights for reducing the cost of capital.
Develop advanced data pipelines and analytics to support finance, accounting, and product growth initiatives.
Create ELT processes and SQL queries to bring data to the data warehouse and other data sources.
Develop data-driven finance products that accelerate funding capabilities and automate accounting reconciliations.
Own and evolve data lake pipelines, maintenance, schema management, and improvements.
Create new features from scratch, enhance existing features, and optimize existing functionality.
Collaborate with stakeholders across Finance, Product, Backend Engineering, and Data Science to align technical work with business outcomes.
Implement new tools and modern development approaches that improve both scalability and business agility.
Ensure adherence to coding best practices and development of reusable code.
Constantly monitor the data platform and make recommendations to enhance architecture, performance, and cost efficiency.

We are seeking an AI/ML Lead Engineer to join our R&D division and drive the adoption of advanced machine learning and AI solutions across our financial and operational domains. In this role, you will design and implement intelligent models that increase efficiency in collections and payments processing and build AI-driven tools that enhance call center performance and agent productivity.
We are looking for a hands-on, independent team player with strong foundations in data science, computer science, and backend engineering, combined with excellent analytical and algorithmic skills. This role is diverse and impactful, spanning machine learning development, data analysis, and backend engineering, with the opportunity to shape our AI/ML strategy for servicing and operations.
As part of the R&D group, you will work closely with engineers and product managers, leading and implementing AI/ML initiatives that directly align with our business goals and innovation vision.
Key Responsibilities:
Lead the design, development, and deployment of ML models to optimize collections and payments processing.
Develop and integrate AI-driven tools to improve call center efficiency and agent productivity.
Collaborate cross-functionally with engineers, product managers, and stakeholders to translate business needs into technical solutions.
Drive data analysis and modeling efforts, leveraging advanced algorithms to uncover insights and improve decision-making.
Ensure scalability, quality, and compliance of all AI/ML solutions within the financial and servicing domains.
Stay up to date with emerging AI/ML technologies and assess their potential impact on the companys strategy.
Mentor and guide team members, fostering knowledge-sharing and technical excellence.