Swaps Java Developer

Role: Swaps Java Developer
Location is New York City 2-3 days/week
Local candidates strongly preferred, interview will be f-f for final round (after 2-3 video rounds, will likely have a coding test)


This contract has an initial duration of six months with likely extension - could convert to FT after 6 months
Key skills: Java, Equities Financing, Swaps, Nuvo Prime and /or SwapOne, some sort of AI platform like DL4J is a big plus, Murex is also a plus
Really need more of a front office type of person (i.e., working with traders)


Job Summary:
We are seeking a hands-on Java Developer to join our Equities Financing Technology team, with a focus on Swaps and synthetic financing products. The ideal candidate will have experience building and supporting trading and lifecycle platforms, and be familiar with vendor tools like Nuvo Prime and SwapOne. Exposure to machine learning or AI platforms such as DL4J is highly desirable.


Key Responsibilities:
Design, develop, and enhance Java-based systems for swaps lifecycle and synthetic equity financing.
Collaborate with traders, product controllers, and operations to deliver front-to-back solutions.
Integrate with vendor platforms (e.g., Nuvo Prime, SwapOne) to streamline trade flow and lifecycle management.
Leverage AI/ML libraries or platforms (e.g., DL4J) for analytics, trade matching, or optimization use cases.
Ensure high availability, performance, and scalability of the trading systems.
Participate in code reviews, performance tuning, and support rotational duties.


Required Skills & Experience:
Strong core Java development skills with multi-threading and low-latency architecture experience.
Solid understanding of Equity Swaps, Total Return Swaps, or synthetic financing products.
Prior experience in Equities Financing or Prime Brokerage domains.
Familiarity with Nuvo Prime, SwapOne, or other industry-standard swaps platforms.
Experience with AI/ML tools in Java, such as DL4J or other relevant libraries (TensorFlow, PyTorch via Java wrappers).
Experience integrating with upstream and downstream systems (e.g., risk, P&L, settlements).
Strong analytical and communication skills; ability to work in a fast-paced environment.


Preferred Qualifications:
Exposure to Python or data science platforms is a plus.
Experience with Murex or similar risk/trade processing platforms.
Prior experience with Kafka, REST APIs, and messaging protocols (e.g., FIX).
Understanding of regulatory and margin implications in synthetic financing.