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Salary: USD 130,000 - 160,000 / annual
Octaura is transforming large, underdigitized financial markets—loans, CLOs, and related asset classes—by applying AI, real-time data, and modern software infrastructure. Trillions of dollars still move through phones, emails, and PDFs; Octaura is building the platform to automate and digitize these workflows.
As an AI/ML Software Engineer, you will design, build, deploy, and maintain production-grade AI systems that power critical financial products. The role bridges applied machine learning and backend software engineering: you'll develop and train ML models using Python frameworks (scikit-learn, PyTorch, TensorFlow), architect agentic AI systems with multi-agent workflows and tool-use pipelines, and build robust backend services in Java and Spring Boot that integrate these models into production.
Key responsibilities include:
- Design and maintain production backend services using Java, Spring Boot, Kafka, Redis, PostgreSQL, and Redshift
- Develop, train, evaluate, and deploy machine learning models in Python
- Architect agentic AI systems, including multi-agent workflows and Model Context Protocol (MCP) integration
- Build data pipelines and feature engineering workflows for ML training and inference
- Implement MLOps practices: model versioning, monitoring, A/B testing, automated retraining
- Collaborate with data scientists, platform engineers, and product teams to translate business problems into AI/ML solutions
- Evaluate and select AI/ML technology stack components, including LLM orchestration and model serving frameworks
- Stay current with LLMs, retrieval-augmented generation (RAG), and agent frameworks
- Write clean, testable, well-documented code and participate in code reviews
You'll need 2+ years of software engineering experience with strong Java and Spring Boot proficiency, hands-on Kafka experience, solid understanding of PostgreSQL and data warehousing (Redshift), and Redis expertise. On the ML side, 2+ years of hands-on Python-based AI/ML development, familiarity with supervised/unsupervised learning and model evaluation, experience with ML frameworks, understanding of LLMs and generative AI (prompt engineering, fine-tuning, RAG), and exposure to agentic AI patterns and orchestration frameworks (LangChain, LangGraph, CrewAI, Spring AI).
The role is based in the New York City office (5 Penn Plaza) with a hybrid schedule: four days in-office (Monday–Thursday) and one day remote (Friday).
