AI Senior Solution Specialist
Marsh & McLennan Companies2 hours ago
Singapore, SingaporeSenior
Responsibilities
- Design and implement end-to-end AI solutions for complex business use cases with production-ready code, scalability, and quality.
- Define solution architectures for AI initiatives across Marsh Risk Asia in alignment with business needs, enterprise standards, governance, and regional priorities.
- Translate business opportunities into scalable and supportable AI solution designs and implementations.
- Develop proof-of-concepts and architecture patterns for knowledge assistants, document intelligence, workflow automation, decision support, and agentic systems.
- Build AI/ML pipelines, model integrations, APIs, and supporting services using Python and other programming languages.
- Architect data pipelines, model-serving strategies, and integration patterns for enterprise-scale AI deployments.
- Evaluate AI technologies, tools, frameworks, and integration approaches based on scalability, maintainability, security, business value, and enterprise fit.
- Collaborate with Global AI Architects and mentor engineering teams on AI/ML practices, architecture decisions, and technical excellence.
Requirements
- 10+ years of technology experience, including significant hands-on experience in solution architecture, AI/ML engineering, enterprise application design, and system delivery.
- 5+ years of experience designing, developing, and deploying AI/ML solutions in production enterprise environments.
- Expert Python proficiency and strong proficiency in at least two additional 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 building generative AI and LLM-based applications, including prompt engineering, RAG, and agentic AI frameworks.
- Advanced MLOps experience covering model versioning, deployment pipelines, and AI lifecycle management.
- Expertise in AI governance and responsible AI, including bias detection, explainability, and model risk management.
- A master's degree in Computer Science, Data Science, Machine Learning, AI, or a related field is preferred.
- Experience in insurance, financial services, or another heavily regulated industry is preferred.
- Experience with insurance use cases, high-throughput and low-latency AI systems, AI platform teams, or AI centers of excellence is preferred.
- Relevant certifications such as AWS ML Specialty, Azure Solutions Architect, GCP Professional ML Engineer, or TOGAF are preferred.
Benefits
- Professional development opportunities, interesting work, and supportive leaders.
- Inclusive and flexible work environment with opportunities to create solutions and have impact.
- Career opportunities, benefits, and rewards intended to support employee well-being.
- Hybrid work arrangement in Singapore requiring at least three days per week in the local office or onsite with clients; office-based teams also identify at least one weekly anchor day.
Tech Stack
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