AI Engineer
Marsh & McLennan Companies2 hours ago
Dublin, IrelandSenior
Responsibilities
- Run one or two focused proofs of concept at a time to evaluate emerging AI technologies against Marsh use cases.
- Select and recommend technologies for adoption with clear guidance on when and why to use them.
- Productionize selected capabilities using repeatable Crossplane infrastructure-as-code patterns across AWS, Azure, and other hyperscaler clouds.
- Configure GitHub Copilot, Claude Code, Cursor, and other AI coding agents to use technologies correctly and follow Marsh standards.
- Become a subject-matter expert in AI technology domains such as memory and retrieval, agent orchestration, guardrails and safety, or MCP tooling.
- Coach peers, engage business units through architecture forums and developer councils, and share knowledge through blogs, demos, and presentations.
Requirements
- Strong software engineering experience in Python and/or TypeScript with a record of shipping production systems.
- Hands-on experience integrating, deploying, instrumenting, and operating LLM-based systems, including prompt engineering, retrieval-augmented generation, function calling, agent frameworks, evaluation, and observability.
- Experience writing infrastructure-as-code for cloud deployments; Crossplane is preferred, while Terraform, Pulumi, or CDK experience is transferable.
- Comfort working across AWS, Azure, and GCP rather than specializing deeply in one cloud.
- Strong intellectual curiosity, high standards, rapid learning ability, and the ability to evaluate new technology and form clear recommendations.
- Excellent communication skills for explaining complex technical decisions to engineers and non-technical stakeholders.
- Preferred experience includes Rust or Go, Model Context Protocol, Crossplane or Kubernetes infrastructure management, AI safety and guardrails, AI coding assistants, and fine-tuning or adapting foundation models.
Benefits
- Professional development opportunities, interesting work, supportive leaders, and access to AI models, tools, tokens, and related resources.
- Inclusive and flexible work environment with equal-opportunity and reasonable-accommodation support.
- Hybrid work arrangement with remote flexibility; colleagues are expected to work in their local office or onsite with clients at least three days per week, with office-based teams designating at least one weekly anchor day.
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