Caseware is one of Canada's original Fintech companies, having led the global audit and accounting software industry for over 30 years, with more than 500,000 users across 130 countries and available in 16 different languages. While you might not have heard of us (yet) over 36,000 accounting and audit professionals list Caseware as a skill on their LinkedIn profiles!
At Caseware, we are building the next generation of intelligent, cloud-based solutions that empower auditors, accountants, and financial professionals worldwide. We are now looking for a Software Developer – AI Platform to join our growing team and help shape the foundation of a reusable agentic AI platform. This platform will be a cornerstone of our AI strategy, designed to enable the rapid, safe, and scalable deployment of AI capabilities across Caseware Cloud.
As part of this team, you’ll be working at the intersection of cutting-edge AI research, robust software engineering, and enterprise-grade cloud architecture. Your contributions will help us create standardized, reusable components and guardrails that make AI adoption faster, safer, and more impactful for our product teams and customers. If you’re passionate about building platforms that accelerate innovation and want to contribute to a forward-looking AI ecosystem, we’d love to have you on the journey.
❗This is a full-time, permanent position.
📍 Location:
This position is available to all applicants eligible to work in Canada and residing in Toronto, Ontario. This is a hybrid role, working from office 3 days a week from our Downtown, Toronto office.
What you will be doing:
Design and build reusable platform components — including prompt/schema design, RAG pipelines, grounding connectors, and agentic execution patterns (task orchestration, tool invocation, and workflow runtime primitives) — to deliver reliable, context-aware LLM interactions and accelerate product team's ability to ship AI-powered features Build evaluation systems for LLM-based features — including LLM-as-a-judge, structured evals, regression suites, and automated reliability/safety checks — to ensure consistent behavior, measurable quality, and dependable customer outcomes Stay current with emerging AI and cloud technologies, lead proof-of-concepts, and translate findings into strategic guidance that informs platform roadmaps and long-term architectural decisions Take ownership of features and solutions across the entire software development lifecycle — from design and implementation to testing, deployment, and ongoing maintenance. Provide technical mentorship to junior developers through code reviews, pair programming, and collaborative solution design Maintain clear, current technical architecture documentation and enforce development best practices to protect the integrity of the codebase Drive operational excellence by identifying recurring issues and eliminating root causes that impact customers and internal teams Partner with DevOps/DevSuccess to improve your team’s build processes, test automation, and CI/CD pipelines Participate in the prioritization and reduction of technical debt of the system your team manages Participate in 24/7 production support rotation for your team’s systems, delivering thorough post-mortems and root cause analysis for major client impact incidents
What you’ll bring:
1–2+ years of practical experience developing LLM-powered systems, including retrieval-augmented generation (RAG), prompt/context engineering, agent orchestration, and tool use -- with experience applying evaluation methods (e.g. LLM-as-a-judge, structured tests) and implementing security and guardrails such as safety filtering, validation, and input/output sanitization 5+ years of experience writing production-grade front-end applications using TypeScript/Angular (or other related modern front-end technology) 5+ years of experience with API microservice development using TypeScript / NestJS (or other related modern JavaScript server frameworks) Proven experience designing and deploying solutions in public cloud environments (preferably AWS), with an understanding of cloud-native services and infrastructure-as-code practices Demonstrated aptitude for writing effective LLM prompts and instructions, and a solid understanding of prompt engineering patterns in real-world use 
