AI Engineer
Cumberland provides institutional OTC liquidity and market-making services for digital assets, offering 24/7 access to deep cryptocurrency and stablecoin markets.
About Cumberland
Cumberland provides liquidity solutions for institutional investors in the cryptoasset space through OTC spot trading, exchange-traded derivatives, bilateral options, and non-deliverable forwards. Founded as the crypto division of DRW Holdings, Cumberland operates globally with offices in Chicago, London, and Singapore, offering 24/7 trading access through voice markets, electronic platforms, and API connections.
Skills
About the Role
You will join a collaborative multi-asset trading team where you drive end-to-end development of AI infrastructure and AI-driven applications, from proof-of-concept to production deployment and maintenance. You will collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for AI and ML. You will implement automated systems for continuous training, validation, and monitoring of models, provide technical leadership in selecting and optimizing AI/ML frameworks, create and maintain feature pipelines, feature stores, and model stores, and develop frameworks for scalable, reproducible research. You will also troubleshoot performance bottlenecks and optimize GPU and CPU resource usage.
Requirements
- Bachelor's or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
- 3+ years of experience working with machine learning and artificial intelligence technology
- Strong understanding of core machine learning and artificial intelligence concepts
- Excellent programming skills in Python
- Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models
- Hands-on experience with MLOps and AIOps infrastructure and tooling
- Proficient in problem-solving and analytical reasoning
- Exceptional communication and collaboration skills
- Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX
- Experience with Large Language Models, including RAG and fine-tuning techniques
- Familiarity with compute infrastructure necessary to support operating AI and ML technology
Responsibilities
- Drive end-to-end development of AI infrastructure and AI-driven applications from proof-of-concept to production deployment and maintenance
- Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact AI and ML opportunities
- Implement automated systems for continuous training, validation, and monitoring of models
- Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools
- Create and maintain feature pipelines, feature stores, and model stores
- Develop frameworks to enable scalable, reproducible research
- Troubleshoot performance bottlenecks, conduct root-cause analyses, and optimize GPU or CPU resource usage
