Staff AI Corporate Engineer

Laurel is on a mission to return time. As the leading AI Time platform for professional services firms, we’re transforming how organizations capture, analyze, and optimize their most valuable resource: time. Our proprietary machine learning technology automates work time capture and connects time data to business outcomes, enabling firms to increase profitability, improve client delivery, and make data-driven strategic decisions. We serve many of the world's largest accounting and law firms, including EY, Aprio, Crowell & Moring, and Frost Brown Todd, and process over 1 billion work activities annually that have never been collected and aggregated before Laurel’s AI Time platform.

Our team comprises top talent in AI, product development, and engineering—innovative, humble, and forward-thinking professionals committed to redefining productivity in the knowledge economy. We're building solutions that empower workers to deliver twice the value in half the time, giving people more time to be creative and impactful. If you're passionate about transforming how people work and building a lasting company that explores the essence of time itself, we'd love to meet you.

Position Overview

We are seeking a Corporate AI Engineer to join our Business Technology team with a specialized focus on enterprise AI enablement and infrastructure. In this role, you will design and operate the internal software factory that empowers employees to build, test, and publish AI agents; manage the hosting infrastructure for AI-generated web experiences; help define and enforce company-wide AI standards; and own the core AI infrastructure layer — covering usage analytics, spend governance, settings configuration, and model management. You will be the connective tissue between cutting-edge AI tooling and every internal team that depends on it.

Key Responsibilities

1. Corporate AI Software Factory

• Design, build, and maintain the internal developer platform that enables employees to create, test, and publish AI agents and automated workflows.

• Develop self-service tooling, templates, and scaffolding so non-engineering teams can safely deploy AI-powered applications.

• Own the CI/CD pipelines specific to AI agent delivery, ensuring repeatable, auditable, and secure release processes.

• Build and manage hosting infrastructure for AI-generated and AI-assisted web pages and internal portals.

2. AI Standards & Governance

• Establish guardrails for model access, data handling, prompt safety, and output validation across internal AI deployments.

• Act as an internal subject-matter expert and advisor, educating teams on best practices for responsible AI integration.

• Maintain a living AI standards playbook, updated as models, regulations, and organizational needs evolve.

3. AI Infrastructure & Platform Engineering

• Own and operate the company's central AI infrastructure layer, including usage dashboards, spend tracking, and budget alerting.

• Manage model configurations, API gateway settings, rate limits, and version-management across multiple AI providers.

• Implement observability tooling (logging, tracing, cost attribution) for all AI workloads to provide clear visibility to stakeholders.

• Evaluate and onboard new AI models, tools, and providers, managing the full lifecycle from pilot to production.

• Design and maintain the MCP (Model Context Protocol) server ecosystem, enabling structured tool-use and agent-to-service integrations.

4. Internal User Enablement

• Partner with business units to identify AI automation opportunities and translate them into production-ready tooling.

• Provide technical support, documentation, and training to internal builders using the AI platform.

• Maintain developer documentation, runbooks, and onboarding guides for the corporate AI ecosystem.

Required Qualifications

• 3–6 years of software engineering experience in a corporate or enterprise environment.

• Hands-on experience with AI ecosystem enablement, including one or more of: Claude (Anthropic), OpenAI Codex / GPT APIs, AI agent frameworks, and MCP (Model Context Protocol) integrations.

• Proficiency building and operating internal developer platforms, software factories, or CI/CD infrastructure.

• Experience with cloud infrastructure (AWS, Azure, or GCP), container orchestration (Kubernetes/Docker), and infrastructure-as-code (Terraform or similar).

• Solid understanding of API gateway patterns, rate limiting, authentication/auth