Position Overview:Sagard is on a journey to become an AI-first organization. We believe artificial intelligence will reshape the workplace by automating repetitive tasks and enabling teams to focus on higher-value, strategic work.
The AI Builder / Forward Deployed Engineer will work closely with business teams across investment, finance, compliance and operations to identify high-value opportunities and build practical AI-enabled solutions. As part of a small and agile AI and Data team, the role combines technical development, experimentation and cross-functional collaboration to rapidly prototype, deploy and scale solutions that improve how work is done across the firm.
This is fundamentally a full-stack engineering role. The majority of the work involves building the data pipelines, system integrations, and foundational infrastructure that make AI solutions possible -- not just the AI layer itself. Strong engineering fundamentals matter as much as AI fluency here.
Compensation range: CAD$90,000 to CAD$130,000Responsibilities:- Partner with business units across investment, finance, compliance and operations to understand operational problems, scope technical solutions, and rapidly prototype and iterate based on real-world feedback.
- Design and build data pipelines that move, transform, and prepare data from internal systems for use in AI and analytics workflows.
- Own end-to-end integrations between internal platforms and third-party APIs, including authentication, error handling, and ongoing maintenance.
- Contribute to foundational infrastructure decisions -- data storage, service architecture, deployment patterns -- as the firm's data and AI platform matures.
- Build and deploy production-ready AI applications, including RAG pipelines, agentic workflows, and LLM integrations, with a pragmatic eye toward what actually works at scale.
- Ensure solutions meet production standards for security, access controls, responsible AI usage, and ongoing maintainability.
Qualifications:- Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical experience.
- 2-5 years of experience in software engineering roles, with demonstrated ownership of production systems end-to-end.
- Solid full-stack engineering fundamentals -- backend services, relational and document databases, REST and webhook integrations, async patterns, and cloud deployment.
- Hands-on experience building and maintaining data pipelines and system integrations in production environments, not just application-layer prototypes.
- Practical experience with LLM-based systems in production -- including RAG pipelines, agentic workflows, and LLM integrations -- and sound judgment on when AI adds value over conventional approaches.
- Curious, self-directed, and biased toward action -- able to work directly with non-technical stakeholders, move fast in ambiguous situations, and drive problems to resolution without heavy oversight.
- Preferred: experience in financial services or alternative asset management, familiarity with structured financial data and compliance constraints.
