Description
We are seeking a hands-on, business-facing engineer to join our team. In this role, you will partner directly with some of the most sophisticated quantitative researchers, developers, and portfolio managers in the industry.
Our team is a specialized group of engineers operating at the intersection of technology and quantitative finance. We function as an internal centre of excellence, providing expert-level solutions, architecture, and hands-on development in AI, Cloud (AWS/GCP), DevOps, and high-performance computing.
As a forward deployed software engineer, you will be responsible for translating complex research requirements into robust, scalable, and secure technical architectures across on-prem, hybrid, and cloud environments. You will write high-quality, production-ready code across the full stack, including Python libraries, infrastructure-as-code (Terraform), CI/CD pipelines, automation scripts, and ML/AI proof-of-concepts.
You will also develop and maintain our suite of managed products, reusable patterns, and best practice guides to provide self-service options and accelerate onboarding for new and existing teams. Additionally, you will act as the primary technical point of contact for embedded engagements, owning projects from discovery and planning through to implementation, knowledge transfer, and support.
To succeed in this role, you will need to have a deep understanding of computer science principles, including data structures, algorithms, and system design. You will also need to have experience working with cloud providers, such as AWS or GCP, and be familiar with infrastructure-as-code concepts. Excellent verbal and written communication skills are also essential, as you will need to build strong relationships with stakeholders and articulate complex ideas to diverse audiences.
Innovative thinking and a passion for AI/ML and its practical applications are highly desirable. Experience designing systems and architectures from ambiguous business needs, as well as experience with scheduling or asynchronous workflow frameworks/services, is also preferred.