Carlyle is hiring an DevEx Platform Engineer to join our Enterprise Technology team. In this role, you will partner with engineering teams, product owners, and other business stakeholders to build, operate, maintain, and troubleshoot the AI-enabled developer platforms used across our global technology organization.
As an DevEx Platform Engineer, you will play a pivotal role in enabling modern AI-driven development across our engineering organization, supporting a development community of over 500 users. Your work will directly shape how Carlyle’s engineers design, build, test, and ship software in an AI-native way. You will split your time between building and running these platforms—writing code, building agentic frameworks, integrating AI coding assistants into the SDLC, and creating the platforms, registries, and abstractions that make AI-driven development safe, repeatable, and scalable inside the firm, while owning their reliability and performance in production.
You will be instrumental across the full lifecycle of these platforms, from evaluating emerging tooling and prototyping new agentic workflows to operating them as production services. You will define and maintain the SLOs, observability, and response practices that keep the platforms dependable, troubleshoot issues as they surface, and continuously look for ways to reduce operational toil so engineers can rely on our platforms the same way they rely on any other production system.
You will work with application engineering, cloud operations, security, and architecture teams to identify friction in the developer experience and deliver paved-road platforms that make AI-driven practices the default way of working at Carlyle. You will also promote these practices across the firm, partnering with engineering teams to drive adoption and measurable productivity gains.
The ideal candidate has the following skills and competencies:
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Operational Reliability: Treats running the platform as a first-class responsibility. Experience defining SLOs and SLIs, instrumenting services with metrics, logging, and tracing, responding to incidents, and conducting blameless postmortems. Reduces operational toil through automation.
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Observability: Hands-on experience with modern observability platforms (Datadog, Grafana, OpenTelemetry, or similar). Builds dashboards, alerts, and traces that surface the right signals at the right time, and partners with engineering teams to instrument their services for production readiness.
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Communication: Able to articulate the value of AI and platform investments to a range of stakeholders, and to build credibility with both technical and non-technical colleagues.
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Hands-On Engineering: Spends a significant portion of working hours writing and automating code. Comfortable with production code, building integrations, debugging pipelines, and shipping features end-to-end across distributed systems.
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Adaptability: Comfortable with ambiguity and committed to ongoing learning as the AI tooling landscape evolves. Looks for practical ways to improve the developer experience.
In-Office Requirement: 4 days per week
