Position OverviewWe 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 Responsibilities1. 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/authorization (OAuth2, API keys, RBAC).
- Demonstrated ability to translate governance requirements (security, compliance, policy) into technical controls.
- Strong communication skills - able to write clear documentation and present technical concepts to non-technical stakeholders.
Preferred Qualifications- Direct experience with Anthropic Claude APIs, Claude.ai for Teams/Enterprise, or building Claude-powered agents.
- Familiarity with MCP server development and integration patterns for agentic tool-use.
- Exposure to AI model management lifecycle: fine-tuning, evaluation, versioning, and rollback strategies.
- Experience with observability stacks (Datadog, Grafana, OpenTelemetry) applied to LLM / AI services.
- Prior role as an internal platform or DevOps engineer supporting developer communities.
AI Ecosystem Familiarity (Desired)Candidates should have working knowledge of - or be eager to rapidly develop expertise in - the following technologies:
- Claude (Anthropic) - API usage, prompt engineering, Claude.ai Enterprise administration
- MCP (Model Context Protocol) - server authoring, tool definitions, agent-to-service wiring
- OpenAI Codex / GPT APIs - integration patterns, function calling, fine-tuning pipelines
- AI Agents & Orchestration - multi-step reasoning agents, tool-use loops, agentic workflows
- OpenClaw - open-source AI tooling and interoperability frameworks
