AI Solution Specialist

Company:

Marsh

Description:

We are seeking an experienced AI Engineer to join our Asia Digital team at Marsh in Singapore. This is a hybrid role requiring at least three days a week in the office.

You will design and develop AI solutions for complex business use cases across Marsh Risk Asia, partnering with business, product, engineering, data, and architecture teams to build production-grade AI-enabled solutions with a focus on code quality, scalability, and responsible deployment.

We will count on you to:

Design and architect AI solutions for use cases including knowledge assistants, document intelligence, workflow automation, decision support, and predictive analytics.

Write clean, maintainable, production-quality code in Python, following best practices and design patterns.

Develop end-to-end AI pipelines including data preprocessing, model development, prompt engineering, retrieval workflows, and API design.

Partner with stakeholders to understand requirements, assess feasibility, and translate needs into working AI-enabled solutions.

Prototype, test, and refine AI models, prompts, and workflows to optimize performance, reliability, and business value.

Ensure solutions comply with Marsh requirements for security, privacy, governance, and responsible AI practices.

What you need to have:

4+ years of experience in AI/ML development, data science, or AI engineering.

Proven hands-on experience deploying AI/ML solutions in production enterprise environments.

Strong Python proficiency plus at least one additional language (Java, TypeScript, Scala, Go, or similar).

Hands-on experience with generative AI and LLM-based applications including prompt engineering, fine-tuning, and RAG.

Familiarity with LLM ecosystems such as OpenAI, Hugging Face, or Anthropic, and vector databases such as Pinecone, Weaviate, or Milvus.

Demonstrated experience with at least one cloud platform (AWS, Azure, or GCP) for deploying AI solutions.

Understanding of enterprise security, data governance, and responsible AI principles.

What makes you stand out:

Master's degree or advanced certification in Machine Learning, AI, Data Science, or Computer Science.

Experience in insurance, financial services, or another regulated industry.

Familiarity with document processing, knowledge management, intelligent automation, or intelligent search.

Experience with MLOps, multi-agent systems, or moving AI solutions from prototype to production at scale.

Why join our team:

We help you be your best through professional development opportunities, interesting work and supportive leaders.

We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.

Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.

Marsh Risk is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information about Marsh Risk, visit marsh.com, or follow us on LinkedIn and X.

Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.

Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.