AI/ML Engineer (Python)
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Role details
Tech stack
Job description
- 45% - Python Engineering: Design, build, and maintain production-grade Python systems that power proprietary forecasting and market-intelligence tools.
- 30% - AI/ML Development: Develop, integrate, and deploy AI/ML models that support commodity, real-world-asset, and financial-instrument price forecasting.
- 15% - Collaboration & Integration: Partner with engineering, quantitative, and product stakeholders to translate complex requirements into scalable, well-architected solutions.
- 10% - Quality & Reliability: Write testable, maintainable code and support the performance, accuracy, and reliability of deployed systems.
Tech Environment
- Language: Python (primary)
- Focus: AI/ML - model development, integration, and deployment
- Domain: Commodity, real-world-asset, and financial-instrument forecasting
Requirements
- Strong professional experience building production Python applications
- Hands-on AI/ML background (model development, integration, or deployment)
- Solid software engineering fundamentals - testing, version control, code quality
- Ability to work onsite in Wilmington, NC (5 days; 2-day hybrid available for longer commutes)
Nice to Have
- Experience in fintech, trading, quantitative finance, or commodity markets
- Background in applied mathematics, physics, or a quantitative discipline
- Familiarity with cloud platforms and model-serving/MLOps infrastructure
Benefits & conditions
Medical Insurance Dental Benefits Vision Benefits Paid Time Off (PTO) 401(k) Match
About the company
Our client, a fintech company building proprietary deterministic-mathematics and AI systems that forecast the pricing of commodities, real-world assets, and financial instruments, is looking for a Senior AI/ML Engineer with strong Python skills to join their engineering team. In this role, you’ll help build the forecasting systems that power market intelligence for hedge funds and trading desks.
Our client is seeking someone who thrives on solving hard quantitative problems, writing production-grade Python, and applying AI/ML to real market outcomes.
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