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Octaura

AI/ML Software Engineer

New York$130k–$160kmidAdded today

About this role

Octaura seeks an AI/ML Software Engineer to build production-grade AI systems for financial markets infrastructure. You'll develop end-to-end machine learning applications using Java/Python, design agentic AI workflows, and deploy models into mission-critical systems serving loans and asset management.

What you'll do

  • Design and maintain production backend services powering AI/ML using Java, Spring Boot, Kafka, Redis, PostgreSQL, and Redshift
  • Develop, train, evaluate, and deploy ML models using Python frameworks like scikit-learn, PyTorch, or TensorFlow
  • Architect agentic AI systems with multi-agent workflows, tool-use pipelines, and MCP integration
  • Build data pipelines and feature engineering workflows for ML training and inference
  • Implement MLOps practices including model versioning, monitoring, A/B testing, and automated retraining
  • Collaborate with data scientists, engineers, and product teams to translate business problems into AI solutions

What they're looking for

  • Java and Spring Boot (2+ years)
  • Python for ML development (2+ years)
  • Apache Kafka and event-driven systems
  • ML frameworks: scikit-learn, PyTorch, TensorFlow, XGBoost
  • PostgreSQL and data warehousing (Redshift)
  • LLMs, RAG, and generative AI concepts
  • Agentic AI frameworks: LangChain, LangGraph, CrewAI, Spring AI
  • Microservices, REST APIs, and distributed systems

Benefits

  • Equity and year-end bonus
  • Full range of medical benefits
  • Hybrid work: 4 days NYC office (5 Penn Plaza), 1 day remote
  • High-impact role shaping financial markets infrastructure
  • Collaborative team environment
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Octaura

Octaura builds an electronic trading platform serving the syndicated loan and CLO markets. The company is hiring Software Engineers, Platform Support Engineers, and Cloud Operations Engineers to develop, maintain, and operate its trading systems.

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Likely interview questions

  • Walk us through a production ML system you built end-to-end—how did you handle model deployment, monitoring, and retraining?
  • Describe your experience building event-driven systems with Kafka. How did you handle scaling and data consistency?
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