AI/ML Software Engineer

Octaura•New York, NY

3d•$130,000 - $160,000•Hybrid

About The Position

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. 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).

Requirements

2+ years of software engineering experience with strong proficiency in Java and Spring Boot.
Experience building event-driven systems using Apache Kafka.
Solid understanding of relational databases (PostgreSQL) and data warehousing (Redshift or equivalent).
Hands-on experience with Redis for caching, queuing, or session management.
Strong grasp of distributed systems, REST API design, and microservices architecture.
2+ years of hands-on experience in AI/ML development using Python.
Familiarity with core ML concepts: supervised/unsupervised learning, model evaluation, feature engineering, and hyperparameter tuning.
Experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost.
Understanding of LLMs and Generative AI concepts, including prompt engineering, fine-tuning, and RAG architectures.
Exposure to Agentic AI patterns — tool use, planning, multi-step reasoning, or agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, Spring AI, or similar).

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.
Implement MLOps practices — including model versioning, monitoring, A/B testing, and automated retraining pipelines.
Stay current with the rapidly evolving AI/ML landscape, including large language models (LLMs), retrieval-augmented generation (RAG), and agent frameworks.
Write clean, testable, and well-documented code; participate in code reviews and foster engineering best practices.