Dev Ops Engineer Lead

Description We are looking for a DevOps Lead to own the infrastructure strategy, CI/CD standards, and release engineering for the Agent Stream at Trintech’s AI Platform. The Agent Stream builds production-grade AI agents that power the Autonomous Financial Close. This is a hands-on IC role with stream-level influence — you are not just executing pipelines, you are defining the standards that Senior DevOps Engineers and squad engineers follow, making infrastructure strategy decisions alongside the Software Architect, and owning the on-prem to Azure migration sequencing for the stream. The right person brings deep Kubernetes and CI/CD depth, can design pipelines that meet AI-specific deployment requirements, and raises the engineering bar of every engineer they work alongside. What You’ll Do Infrastructure Strategy & Ownership Own the cloud-native infrastructure strategy for the Agent Stream — Kubernetes cluster configuration, namespace design, resource management, and environment topology across development, staging, and production. Lead the on-prem to Azure migration for Agent Stream services — sequencing what moves first, designing the migration path using Terraform, and maintaining service continuity throughout. This is a stream-level strategic decision, not a task-level one. Define infrastructure-as-code standards for the Agent Stream — Terraform module design, Helm chart structure, Kubernetes manifest patterns, and configuration management practices that Senior DevOps Engineers implement and follow. Ensure infrastructure is designed for resilience — high availability, automated failover, resource isolation between agent services, and graceful degradation under load. CI/CD Pipeline Standards & Ownership Define and own CI/CD pipeline standards across all Agent Stream squads — multi-stage pipeline design covering build, automated testing, security scanning (SAST/DAST), staging gates, and production deployment. Senior DevOps Engineers execute within the standards you set. Design pipelines for AI-specific deployment requirements — model version management, Langfuse trace routing, confidence threshold configuration deployments, and agent configuration change workflows that go beyond standard application deployment. Own release approval workflows and deployment gates — including rollback procedures and audit trail generation that meet compliance requirements. Implement and govern progressive delivery practices — blue-green deployments, canary releases, and feature flag integration. Define when each pattern is appropriate and ensure squads apply them correctly. Own pipeline observability as a stream-level signal — deployment frequency, lead time, failure rate, and MTTR as engineering health metrics reported to the Engineering Manager. Influence Leadership & Technical Direction Provide technical direction for DevOps practice across the Agent Stream — tooling decisions, pipeline patterns, and infrastructure standards, working in…