Senior Python Quant Developer — Hybrid Cloud Grid & ML

Axelon Services Corporation; Jersey City (NJ); Hybrid; USD 125,000 - 132,000; Full time

Job summary

Axelon Services Corporation in Jersey City, NJ seeks an experienced Python Quantitative Developer to architect a scalable, hybrid-cloud pricing platform. You will design a job orchestration system and an event-driven lifecycle pipeline for large-scale grid computations.

The role requires deep Capital Markets knowledge, expertise in Docker/Kubernetes, and strong programming skills in Python and C. A hybrid work setup is available in Jersey City, with on-site collaboration and cloud integration.

Qualifications

• 10+ years of professional experience building high-performance compute grids.
• Degree in a technical field such as CS or Quantitative Finance.
• Deep domain knowledge in Capital Markets and derivatives, with regulatory awareness.

Responsibilities

• Architect and build a next-generation, hybrid-cloud platform for massive-scale pricing calculations.
• Design a sophisticated job orchestration system with dynamic prioritization and resource optimization.
• Develop an event-driven framework for job lifecycle management and result data flow.
• Implement robust error handling and resiliency for large-scale distributed environments.
• Build ML-based monitoring to detect anomalies and predict failures for grid optimization.
• Ensure secure data movement between on-premises systems and public cloud environments.
• Collaborate with quants to onboard and optimize pricing models in distributed computation.

Skills

Python

C

Docker

Kubernetes

Job scheduling

Event-driven architecture

ML anomaly detection

Cloud platforms (AWS/GCP)

Capital Markets

Distributed computing

Education

B.S. in Computer Science or Quantitative Finance

Tools

Docker

Kubernetes

Job description

Axelon Services Corporation in Jersey City, NJ seeks an experienced Python Quantitative Developer to architect a scalable, hybrid-cloud pricing platform. You will design a job orchestration system and an event-driven lifecycle pipeline for large-scale grid computations.

The role requires deep Capital Markets knowledge, expertise in Docker/Kubernetes, and strong programming skills in Python and C. A hybrid work setup is available in Jersey City, with on-site collaboration and cloud integration.