AI/ML Software Engineer

Come build what the market has been missing.  

Octaura is a technology company transforming how some of the world’s largest, least digitized markets operate. In loans and CLOs, trillions of dollars still move through phones, emails, and PDFs because the right infrastructure hasn’t existed — until now. 

We’re applying AI, real-time data, and modern software design to asset classes long overlooked by fintech. Our platforms turn fragmented information into intelligence, manual workflows into automation, and legacy conventions into scalable market infrastructure. 

You’ll join a high-impact, collaborative team building the systems these markets need to become more transparent, efficient, and connected. 

The Role

An AI Engineer at Octaura, is a software professional who develops and implements AI-powered applications by integrating trained machine learning models, APIs, and data pipelines into functional software products. You will design, build, deploy, and maintain applied machine learning systems that power critical products and workflows across our platform. This role focuses on taking AI solutions from concept through production—working with large, complex datasets and ensuring models are reliable, explainable, and performant in real-world financial environments.

You’ll partner closely with product managers, software engineers, and data teams to solve meaningful business problems, improve market efficiency, and introduce AI responsibly into mission-critical systems. Depending on experience, you may take full ownership of complex AI initiatives and help shape Octaura’s ML architecture and best practices.

This role is based out of our New York City office (5 Penn Plaza), with a hybrid schedule of four days in the office (Monday–Thursday) and one day remote (Friday).

Key Responsibilities

• Design, build, and maintain production-grade backend services that power AI/ML capabilities, using Java, Spring Boot, Kafka, Redis, PostgreSQL, and Redshift.
• Develop, train, evaluate, and deploy machine learning models using Python-based ML frameworks (e.g., scikit-learn, PyTorch, or TensorFlow).
• Architect and implement Agentic AI systems, including multi-agent workflows, tool-use pipelines, and integration with Model Context Protocol (MCP) or similar orchestration frameworks.
• Build and maintain data pipelines and feature engineering workflows to support ML training and inference.
• Collaborate with data scientists, platform engineers, and product teams to translate business problems into AI/ML solutions.
• Contribute to the evaluation and selection of the team's AI/ML technology stack, including frameworks for Agentic AI, LLM orchestration, and model serving.