Enterprise AI Architect – Claude Code & Agentic Engineering

Our client is the world's largest human resources consulting firm, headquartered in New York City, with operations across more than 130 countries and major branches in over 40 countries. The organization employs more than 25,000 people globally and provides services to the majority of Fortune 500 companies. We are looking for an experienced Enterprise AI Architect to join a team responsible for designing and building reusable AI capabilities and Claude Code Skills that can be applied across enterprise business processes and software-development workflows.

• Strong hands-on experience with Claude Code in real-world software-development or business-workflow scenarios.
• Proven practical experience creating Claude Code Skills or comparable reusable capabilities for AI coding agents.
• Strong understanding of modern AI engineering and agentic AI patterns, including agentic workflows, AI agent orchestration, tool use, prompt engineering, context engineering, structured instruction design, reusable AI capabilities.
• Strong professional software engineering background with ability to design, implement, review, test, and maintain production-quality solutions.
• Experience translating business requirements and domain knowledge into technical designs, structured workflows, and executable AI instructions.
• Practical experience evaluating, troubleshooting, and improving non-deterministic AI/LLM-based solutions.
• Strong understanding of software architecture and ability to make decisions affecting an entire AI platform or capability.
• Experience defining engineering standards, reusable components, and architectural patterns.
• Ability to establish clear quality criteria for AI Skills and determine acceptance during technical review.
• Ability to distinguish between functionality belonging within individual skills and shared infrastructure.
• Strong technical leadership skills, including reviewing engineers' solutions, providing direction, and resolving blockers.
• Ability to make and clearly communicate architectural decisions including those related to agent harnesses, integration architecture, platform architecture, reusable components, build-versus-adopt strategies, evaluation and release approaches.

Nice to have:

• Hands-on experience with Claude Code plugins and/or MCP servers.
• Experience with Promptfoo, LangSmith, or custom evaluation harnesses.
• Experience working in consulting, professional services, or client-facing technology environments.
• Domain knowledge of HR technology, compensation & benefits, payroll, workforce analytics.
• Experience building internal developer tooling or establishing new technical platforms/capabilities from the ground up.
• Public technical artefacts such as open-source AI/agent tooling, published Claude Code Skills or plugins, MCP servers, conference presentations, or technical articles focused on agentic engineering.

• Work directly with business stakeholders and domain experts to understand business objectives, processes, constraints, requirements, and expected outcomes.
• Analyse business requirements, process documentation, examples, templates, operational knowledge, and other reference materials.
• Identify business workflows and use cases that can be automated, enhanced, or standardized using Claude Code Skills and AI agents.
• Design, implement, test, and maintain reusable Claude Code Skills and related AI capabilities.
• Create structured skill instructions, supporting scripts, reference materials, validation logic, and reusable workflow components.
• Translate complex, ambiguous, or highly contextual business processes into clear, structured, and executable instructions for AI agents.
• Design AI workflows that are reliable, maintainable, reusable, and suitable for enterprise-scale adoption.
• Evaluate AI skill behaviour using realistic business scenarios, test cases, acceptance criteria, and repeatable evaluation frameworks.
• Analyse non-deterministic AI behaviour and continuously improve the accuracy, reliability, consistency, and usability of AI-generated outputs.
• Establish standards and guidelines defining what constitutes a well-designed and production-ready AI Skill.
• Determine which capabilities should be implemented as individual skills and which should become shared infrastructure or reusable platform components.
• Define technical standards for skill development, testing, evaluation, versioning, release management, and backward compatibility.
• Provide technical leadership through architecture reviews, code and solution reviews, technical direction, and problem-solving.
• Unblock engineers and support the team in resolving complex technical and architectural challenges.
• Make and justify platform-level technical decisions related to agent harnesses, integration architecture, reusable components, and build-versus-adopt strategies.
• Own the overall technical shape of the AI capability.