Platform Engineer, Quality and Delivery

Location: Remote | Full-Time Department: Engineering About Kooltra Kooltra is a Canadian fintech company building FX infrastructure and cross-border payment technology for banks and financial institutions. With over a decade in market, we're accelerating our US expansion and shifting toward an AI-first development and delivery model. AI has changed the economics of writing code. The bottleneck is no longer how fast we can build — it's how confidently we can ship. As AI accelerates the volume and velocity of change across our platform, the systems that verify correctness, catch regressions, and keep production observable become the highest-leverage engineering investment we can make. We're hiring an architect-level platform engineer to own that layer end to end. The Role You will design, build, and operate the quality gate system that lets Kooltra ship AI-accelerated code with confidence — in a domain where correctness is non-negotiable. Financial infrastructure doesn't get to "move fast and break things." Your job is to make moving fast and not breaking things a structural property of how we deliver software, rather than a matter of individual discipline. This is a systems-building role, not a QA role. You won't be writing test cases for other people's features — you'll be architecting the automated, layered verification pipeline that every change flows through, from the first property test to production observability. Key Responsibilities Architect a layered quality gate system spanning the full delivery lifecycle: property-based testing, unit and integration testing, contract testing, UI and end-to-end testing, CI/CD pipeline gates, and production observability Champion test-driven development as a design discipline across the engineering organization — and evolve what TDD means when AI agents are writing a growing share of the code Establish property-based testing as a first-class practice for our financial domain logic, where invariants (balances reconcile, events replay deterministically, money is never created or destroyed) matter more than example-based cases Design and own CI/CD pipelines that act as structured, automated gates — every merge and deploy earns its way to production through verification, not review-by-heroics Build the UI and end-to-end test infrastructure that keeps critical user journeys verified continuously, with the reliability and speed engineers actually trust Define the observability architecture — metrics, traces, structured logs, SLOs, and alerting — that closes the loop from "it passed the pipeline" to "it's behaving correctly in production" Make quality gates AI-native: verification systems that validate AI-generated code at the volume and velocity AI development produces, and that AI agents themselves can invoke, interpret, and respond to Set the standards, golden paths, and tooling that make the well-tested path the easiest path for every engineer and every AI agent on the platform Instrument and report on delivery health — escaped defects, flake rates, lead time, change failure rate, time to recovery — and drive them in the right direction Partner with platform, product, and compliance teams to ensure the verification layer supports auditability and our regulatory obligations Required Qualifications Deep experience designing test architecture and delivery systems in enterprise or B2B SaaS environments — you've built quality systems, not just used them Hands-on expertise with property-based testing (e.g., jqwik, Kotest property testing, fast-check, Hypothesis) and a clear view of where it beats example-based testing A genuine test-driven development practice and the ability to teach, model, and scale it as a design discipline Proven experience building and operating UI / end-to-end test infrastructure (e.g., Playwright, Cypress) that stays fast and non-flaky at scale Strong CI/CD architecture experience — designing pipelines as products, with staged gates, ephemeral environments, and deployment strategies like canary or progressive rollout Production observability expertise: metrics, distributed tracing, structured logging, SLOs, and alerting (e.g., Prometheus, Grafana, OpenTelemetry, Datadog) Proficiency in Kotlin and TypeScript, or deep experience on the JVM with the ability to ramp quickly Experience with event-driven architectures (Kafka) and how to test them — contract testing, replay testing, verifying eventual consistency Hands-on Kubernetes (EKS) and AWS experience Genuine enthusiasm for AI-assisted development — and a clear-eyed view of why it makes rigorous automated verification more important, not less Excellent communication skills — this role changes how the whole engineering team works, and that requires influence, not just architecture Nice-to-Have Qualifications Fintech, payments, FX, or treasury operations background — you understand why a rounding error is an incident Experience building verification systems for AI-generated code, or evaluation harnesses for LLM-based systems Mutation testing experience and opinions on measuring test suite effectiveness beyond coverage Formal methods exposure (TLA+, model checking) or design-by-contract experience SOC 2 or similar compliance framework familiarity — evidence automation, change management controls Track record building internal developer platforms, tooling, or golden paths Open-source contributions to testing, CI/CD, or observability tooling Why This Role Matters Every company adopting AI development is about to learn the same lesson: generation is cheap, verification is everything. The quality gate layer is what converts AI acceleration from risk into advantage — and at Kooltra, it protects systems that move real money for real financial institutions. If you believe a great test suite is an act of empathy for every engineer who ships after you, that pipelines should be architected like products, and that AI-first delivery is only as strong as the gates it passes through — we'd love to talk.