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Marsh McLennan

AI Senior Solution Specialist

Posted 10 Days Ago
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In-Office
Marine Parade, SGP
Senior level
In-Office
Marine Parade, SGP
Senior level
Design and deliver end-to-end, production-ready AI solutions and architectures across enterprise use cases. Implement code and pipelines (primarily Python), integrate models and services, build proofs-of-concept for LLMs/generative AI, establish MLOps and governance, and mentor engineering teams while aligning with global AI architecture and security standards.
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Company:Marsh

Description:

We are seeking a talented individual to join our Asia Digital team at Marsh. This role will be based in Singapore. This is a hybrid role that has a requirement of working at least three days a week in the office.

This role sits at the intersection of AI engineering and enterprise solution architecture, with a focus on turning high-value business opportunities into secure, scalable, and production-ready AI capabilities. You will work closely with business, product, engineering, data, and Global AI Architecture teams to design and deliver practical AI solutions that support workflow optimization, document intelligence, knowledge enablement, decision support, and intelligent automation.

 

We will count on you to:

  • Design and implement end-to-end AI solutions for complex business use cases, ensuring code quality, scalability, and production readiness.

  • Define solution architectures for AI initiatives across Marsh Risk Asia, aligned to business needs, enterprise standards, regional priorities, and governance requirements.

  • Partner with business and technology stakeholders to translate high-value AI opportunities into clear, scalable, and supportable solution designs and implementations.

  • Design architecture patterns and develop proof-of-concepts for AI-enabled capabilities such as knowledge assistants, document intelligence, workflow automation, decision support, and agentic systems.

  • Write production-grade code in Python and other relevant languages to implement AI/ML pipelines, model integrations, APIs, and supporting services.

  • Architect data pipelines, model serving strategies, and integration patterns that support enterprise-scale AI deployments.

  • Collaborate with Global AI Architects to align local solutions with enterprise architecture standards, governance expectations, reusable patterns, and platform direction.

  • Evaluate AI technologies, tools, frameworks, and integration approaches and make recommendations based on scalability, maintainability, security, business value, and enterprise fit.

  • Mentor and guide engineering teams on AI/ML best practices, architecture decisions, and technical excellence.

 

What you need to have:

  • 10+ years of technology experience, including significant hands-on expertise in solution architecture, AI/ML engineering, enterprise application design, and system delivery.

  • 5+ years of proven experience designing, developing, and deploying AI/ML solutions in production enterprise environments.

  • Expert-level coding proficiency in Python, with strong proficiency in at least two additional programming languages such as Java, Scala, Go, TypeScript, or C++.

  • Strong background in enterprise solution architecture, cloud platforms, AI/ML frameworks, APIs, microservices, data architecture, and secure enterprise integration patterns.

  • Hands-on experience designing and implementing generative AI and LLM-based applications including prompt engineering, RAG, and agentic AI frameworks.

  • Advanced MLOps experience including model versioning, deployment pipelines, and AI lifecycle management.

  • Expertise in AI governance and responsible AI including bias detection, explainability, and model risk management.

 

What makes you stand out:

  • Master's degree in Computer Science, Data Science, Machine Learning, AI, or a related field.

  • Experience in insurance, financial services, or another heavily regulated industry with complex compliance requirements.

  • Deep familiarity with insurance use cases such as document intelligence, claims processing, underwriting support, fraud detection, or risk modeling.

  • Experience building and scaling AI systems for high throughput and low-latency requirements.

  • Experience building or managing AI platform teams or establishing AI centers of excellence.

  • Relevant certifications: AWS ML Specialty, Azure Solutions Architect, GCP Professional ML Engineer, TOGAF, or similar.

 

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.

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