• Execute cloud projects for our clients • Flexible, self-determined working hours • Work from home (remote) • Take the next step in your career and learn how to lead teams and run a company • Performance- and results-based compensation

• Architect and evolve the internal platform (compute, networking, observability, security, deployment) as a product consumed by engineering and data teams • Provide technical leadership on initiatives that improve DORA metrics, reliability (SLOs, incident response) and developer experience • Establish and evangelize standards, paved roads and internal libraries that multiply team productivity • Maintain and evolve the data infrastructure (pipelines, warehouse, lineage) together with data engineers and data scientists • Design end-to-end observability, security and FinOps solutions • Write RFCs, ADRs and technical documentation that support long-term architectural decisions • Mentor senior and mid-level engineers, raising the team's technical bar • Act as a technical partner to leadership in defining the roadmap and quarterly prioritization

• This engineer will help build a corporate Internal Development Platform (IDP) for software and AI, used by multiple product engineering teams globally. • Design and implement cloud infrastructure as code • Build and maintain CI/CD pipelines • Deploy containerized workloads (Kubernetes) • Establish observability (logs, metrics, alerts) • Create reusable “golden path” templates • Enable secure and scalable deployment patterns • Automate everything possible

• Review and validate competencies and learning objectives for the MLOps / AI Platform Engineer pathway • Validate technical accuracy of instructional slide content covering ML pipelines, model deployment, monitoring, and governance • Review async assets including prompt-alongs and self-paced exercises for technical correctness and appropriate difficulty level for the learner population (experienced customer-facing professionals, not engineers) • Participate in one structured SME review gate (approximately 1 week, early June) • Provide a single round of revision feedback for the LED to implement before QA