2 months ago
Remote, CanadaStaff+
Responsibilities
- Define and maintain organization-wide architecture decisions, AI platform boundaries, persistence standards, shared service kits, and quality gates.
- Lead architecture for large-scale, distributed, customer-facing systems where AI intersects with the product stack, applying domain-driven design to define boundaries.
- Make quantified build-versus-buy decisions involving evaluation, observability, persistence, vector stores, vendors, cost models, and rejected alternatives.
- Establish evaluation standards, drift monitoring, release criteria, resource-isolation controls, agentic workflow guardrails, responsible-AI practices, and auditability.
- Maintain current AI platform choices through dated verification notes, tradeoff analysis, and revisit triggers.
- Set the organization-wide path toward Level 4 and Level 5 agentic development while maintaining payroll and tax-reporting quality and compliance.
- Serve as a technical resource across teams, author RFCs and reviews, unblock high-impact initiatives, and mentor, pair with, and develop senior engineers.
- Contribute to Wagepoint’s software factory by directing and reviewing agentic development, owning specific agents, and improving the factory itself.
- Spend approximately 85% of focus on customer-facing AI products and their platform, with the remainder on internal AI use and the software factory.
Requirements
- 10+ years of professional software engineering experience with progressively increasing impact.
- Multiple years designing and operating production LLM or agentic systems at Staff-level scope or above.
- Proven technical leadership designing distributed systems and microservices for complex domains, with practical knowledge of DDD and Clean Architecture principles.
- Full-stack breadth across modern front-end frameworks and a Python back end; .NET or C# experience is a plus but not required.
- Experience authoring architecture standards and decision records adopted beyond the candidate’s own team.
- Demonstrated quantified build-versus-buy decisions involving named vendors, cost models, and explicitly rejected alternatives.
- Deep knowledge of stateful agent orchestration, MCP, A2A interoperability, evaluation-gated delivery, and vector-enabled persistence including PostgreSQL/pgvector and DiskANN-class indexing.
- A considered model-strategy perspective covering fine-tuned small language models versus frontier APIs and tradeoffs among cost, latency, and control.
- Experience defining boundaries between AI stacks and existing product stacks, including shared service-kit libraries consumed by other teams.
- Security and governance leadership involving resource isolation, agentic workflow guardrails, and responsible AI in a regulated money-movement domain.
- Executive-level communication of platform tradeoffs involving cost, risk, and optionality.
- Strong agency in creating net-new systems and optimizing API performance where established patterns do not exist.
- Demonstrated multiplier effect through mentorship, pairing, unblocking, and growing senior engineers.
- Commitment to directing and reviewing agentic development while maintaining high quality and compliance standards.
Benefits
- Remote-first work with flexibility to work from wherever the employee does their best work.
- Professional development, new experiences, and career-growth opportunities.
- A collaborative, inclusive culture that supports experimentation, learning, and responsible use of AI.
- Virtual interviews and accommodation support throughout the hiring process.
- AI-based tools support initial application review, with final progression decisions made by the People Ops team.
Tech Stack
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