Applied AI Engineer (Core Java, Market Risk)
Montreal, QC (100% Onsite)
Location: Montreal, QC, Canada (100% onsite, no remote or hybrid)
Type: Contract, 12/01/2026 to 11/30/2027
Interview: May require an in-person interview
About the Role
We’re looking for an Applied AI Engineer to join a market risk engineering squad. You’ll build and support core components of a Market Risk platform in Core Java, use AI tools to power agentic and analytical workflows, and mentor junior developers while working with global teams in an Agile environment. Deep Market Risk knowledge isn’t needed up front. You’ll pick it up by working closely with the Risk and Analytics teams.
What You’ll Do
- Design, build, and support key components of the Market Risk platform in Core Java
- Build scalable, high-performance computing solutions for market risk calculations and analysis
- Use AI tools to support agentic and analytical workflows, keeping quality, accuracy, and correctness high
- Work with cross-functional and global teams to deliver quality solutions
- Provide technical leadership and mentorship to junior developers in the squad
- Help evolve risk management tools, frameworks, and processes as business and regulatory needs change
Required Qualifications
- 5+ years building large-scale, production-grade systems, with real depth in Java backend development and distributed system design
- Strong Core Java expertise and a track record of production-grade backend systems
- Solid grounding in data structures, algorithms, and software design patterns
- Strong working knowledge of Linux/Unix environments and scripting
- Experience with relational and/or document-oriented databases
- Ability to turn business and analytical requirements into efficient technical solutions
- Ability to guide junior staff, influence technical direction, and juggle multiple initiatives
- Bachelor’s degree in Computer Science, IT, or a related field (or equivalent experience)
Preferred Qualifications
- Agentic AI development experience, with tools like GitHub Copilot and Claude Code
- Python and Unix in production
- Cloud experience on Azure, AWS, or GCP
- Big data exposure: Apache Spark, Snowflake
- Infrastructure as code: Terraform, CloudFormation
- Containerized deployments and cloud-native architecture
- Agile and DevOps best practices
- Background in financial services, especially Market Risk or FinTech
