1 month ago
Singapore, SingaporeSenior
Responsibilities
- Design and implement end-to-end AI solutions for complex enterprise business use cases.
- Define solution architectures aligned with business needs, enterprise standards, regional priorities, and governance requirements.
- Translate AI opportunities into scalable, supportable solution designs and production implementations.
- Develop proof-of-concepts and architecture patterns for knowledge assistants, document intelligence, workflow automation, decision support, and agentic systems.
- Write production-grade code for AI/ML pipelines, model integrations, APIs, and supporting services.
- Architect data pipelines, model-serving strategies, and enterprise integration patterns.
- Evaluate AI technologies, tools, frameworks, and integration approaches for scalability, maintainability, security, business value, and enterprise fit.
- Collaborate with Global AI Architects and mentor engineering teams on AI/ML practices and architecture decisions.
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 experience with 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 applications using 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.
- A master's degree in Computer Science, Data Science, Machine Learning, AI, or a related field is a preferred qualification.
- Experience in insurance, financial services, or another heavily regulated industry is preferred.
- Experience scaling high-throughput, low-latency AI systems; building or managing AI platform teams; or establishing 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
- Hybrid work in Singapore with at least three days per week in the office or onsite with clients.
- Professional development opportunities, interesting work, supportive leaders, and an inclusive culture.
- Career opportunities and benefits and rewards intended to support colleague well-being.
Tech Stack
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