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Laurel is seeking a Staff AI Corporate Engineer to join the Business Technology team and lead enterprise AI enablement and infrastructure initiatives. This is a high-impact individual contributor role with company-wide scope, responsible for designing and operating the internal software factory that empowers all employees to build, test, and publish AI agents and automated workflows.
Key responsibilities include: designing and maintaining an internal developer platform for AI agent creation and deployment; building self-service tooling and templates for non-engineering teams to safely deploy AI applications; owning CI/CD pipelines for AI agent delivery; managing hosting infrastructure for AI-generated web experiences; establishing governance guardrails for model access, data handling, and prompt safety; acting as internal SME on responsible AI practices; operating the company's central AI infrastructure layer including usage dashboards, spend tracking, and budget alerting; managing model configurations and API gateway settings across multiple AI providers; implementing observability tooling for all AI workloads; evaluating and onboarding new AI models and providers; designing and maintaining the MCP (Model Context Protocol) server ecosystem; partnering with business units to identify AI automation opportunities; and providing technical support and documentation to internal builders.
Required qualifications: 3–6 years of software engineering experience in corporate/enterprise environments; hands-on experience with AI ecosystem enablement (Claude, OpenAI APIs, AI agent frameworks, MCP); proficiency building internal developer platforms, software factories, or CI/CD infrastructure; cloud infrastructure experience (AWS, Azure, GCP); container orchestration (Kubernetes/Docker); infrastructure-as-code (Terraform); API gateway patterns and authentication/authorization; ability to translate governance requirements into technical controls; strong communication and documentation skills.
Preferred: direct Claude API experience; MCP server development; AI model management lifecycle knowledge; observability stacks (Datadog, Grafana, OpenTelemetry); prior internal platform or DevOps engineering roles. The ideal candidate has working knowledge of Claude, MCP, OpenAI APIs, AI agents, and open-source AI tooling frameworks.
