Global Applied Product AI Engineer

Marsh McLennan•Charlotte, NC

1d•Hybrid

About The Position

We are looking for a highly motivated AI Product Engineer to join our growing team in Dublin. This role sits at the intersection of product, engineering, and data, focused on building and scaling AI-powered workflows and internal tools that deliver real business impact. You will work closely with product managers, domain experts, and engineers to translate complex use cases into production-ready AI systems. This is a hands-on role where you will architect, build, and deploy AI features end-to-end, owning system performance across accuracy, latency, cost, and user adoption. We work in an agentic development environment — tools like Claude Code are part of the daily workflow. This means systems judgment and the ability to direct, evaluate, and refine AI-generated code matter as much as raw language fluency.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
3–7 years of experience in software engineering or AI product development (or equivalent demonstrated capability)
Ability to architect and direct AI systems end-to-end, with strong proficiency in Python and comfort across a modern AI stack
Familiarity with JavaScript/TypeScript for browser-based or enterprise tool integrations
Experience with LLM API integration, including prompt design, chaining, and structured outputs
Experience building and deploying scalable applications on cloud platforms such as AWS, GCP, or Azure
Strong problem-solving skills and ability to operate in a fast-paced, ambiguous environment

Nice To Haves

Experience with generative AI applications, NLP, and conversational AI systems
Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG)
Experience with agentic AI patterns, including multi-step reasoning and orchestration frameworks
Experience with MLOps tools and practices
Exposure to product development lifecycle and agile methodologies
Understanding of data privacy, governance, and ethical AI considerations

Responsibilities

Design, build, and deploy AI-driven product features using LLMs and modern AI APIs
Architect and own the data layer: embeddings, vector databases, retrieval pipelines, usage logging, and feedback capture
Develop scalable backend systems and APIs to support AI functionality
Integrate third-party AI tools, APIs, and frameworks (Anthropic, OpenAI, Azure OpenAI) where appropriate
Design and implement evaluation frameworks to measure and improve AI system reliability and output quality
Monitor AI system performance in production and continuously optimize for accuracy, latency, and cost
Design and orchestrate multi-step AI workflows using tool use, memory, and reasoning chains
Collaborate with product and domain experts to translate workflow requirements into working systems
Ensure high standards of code quality, testing, and documentation
Stay up to date with advancements in AI, particularly in generative AI and large language models