Convene Hospitality Group - Director, Cloud Infrastructure

• Rebuild the deployment path. Own CI/CD end to end: build, release, and rollback. Take us from manual deploys to an automated path. • Rebuild the deployment path. • Make it safe, secure and governed. Access control, audit trails, secrets management, environment separation, and a rollback path any engineer can execute. • Make it safe, secure and governed. • Make the platform calls. Source control, CI tooling, orchestration, and infrastructure-as-code patterns. Jenkins is what we run today; what we run next is up to you. Decide, document the trade-off, then go forth and migrate. • Make the platform calls. • Run the AI gateway. Build and operate the layer between our engineers, agents and the models they call: provider routing, key management, rate limits, token metering, and usage logging. Every AI call should be visible, governed, and attributable. • Run the AI gateway. • Enable AI-first, spec-driven development. Build the environments, pipelines and guardrails that let engineers and agents ship with confidence: ephemeral environments, fast feedback, and automated verification. • Enable AI-first, spec-driven development. • Model and manage cloud and AI cost. Forecast what AI systems will cost before we commit and give Finance numbers they can plan against. Keep a standing view of where cloud spend goes and why, and partner with teams to help keep it in check. • Model and manage cloud and AI cost. • Support in-venue networking applications. Each venue relies on network infrastructure for access control, connectivity, and on-site A/V and production. You’ll give our networking team the visibility to monitor and support the applications that keep those venues running. • Support in-venue networking applications. • Build the observability layer. Logging, metrics, alerting, and routing on our Loki/Grafana stack, so every failure reaches a named owner. • Build the observability layer. • Spread what you know and what you do. Coordinate with the product engineers and ensure a level of backup: documented runbooks, a named second operator on every critical path, and dissemination of knowledge and function across product and corporate engineering. Within six months, you can take two weeks off and we can trudge through an emergency without interrupting you. • Spread what you know and what you do. • What We Look For: • What We Look For: • You have 9+ years of experience building and running production cloud infrastructure. • You're deep on AWS: multi-account structure, networking, IAM, and container orchestration on EKS and/or ECS. You've operated production workloads through incidents, upgrades, and growth. • You're fluent in infrastructure as code (Terraform or equivalent) and treat infrastructure changes with the same rigor as application code: reviewed, versioned, reproducible. • You've built or rebuilt a CI/CD pipeline that teams actually use. You know what it takes to move an organization from manual deploys to automated ones, including the trust and process work that comes with it. • You think about security and governance as design inputs rather than a review gate. Secrets, least privilege, audit trails, environment isolation, and who is allowed to push what, where. • You’ve run AI systems in production, from the gateway layer (access control, provider routing, cost attribution, observability) to LLM-backed features, including their evaluation, monitoring, and versioning. Experience with MLOps is a plus as our needs grow. • You can estimate cost and defend the number. You've modeled the spend of an architecture before it was built and adjusted the design when it didn't hold up. • You're high agency. You set direction without waiting for a spec, make platform decisions with real consequences, and then own the migration. You hold positions firmly, change them on evidence, and leave behind a written trail. • You’re a problem solver. Application and data teams seek you out for collaboration on architecture, debugging, and trade-off analysis. • You work in the open and hand things off deliberately. You write the runbook, train the second operator, and measure your success partly by how little depends on you being reachable. • You explain decisions, risks and trade-offs clearly to engineers, non-engineers, and executives. You make teammates better through code review, pairing, and mentoring. • You use AI agents for real multi-step work beyond autocomplete, and you verify what they produce against the real system.