1 month ago
Singapore, SingaporeStaff+
Responsibilities
- Lead requirements workshops with Risk Engineers, subject-matter experts, and insurance risk consulting stakeholders.
- Design high-level architectures and component designs for AI systems covering ingestion, vector indexing, retrieval, LLM orchestration, backend APIs, frontend applications, storage, and monitoring.
- Define AI and retrieval-augmented generation approaches, develop prompt engineering playbooks, and select appropriate technologies.
- Ensure solutions meet enterprise security, compliance, privacy, governance, PII-handling, and responsible AI standards.
- Design user experience flows and wireframes and collaborate with engineering teams on implementation.
- Lead code and architecture reviews, mentor engineering teams, and provide technical oversight throughout delivery.
Requirements
- Expert-level Python proficiency for AI/ML development and production-grade coding, plus proficiency in at least one of TypeScript, Java, Go, or Scala.
- 12+ years of technology experience with significant hands-on expertise in solution architecture, AI/ML engineering, and system delivery.
- At least five years of experience designing, developing, and deploying AI/ML solutions in production enterprise environments.
- Strong background in AI/ML frameworks, generative AI, retrieval-augmented generation, NLP, and document intelligence.
- Experience with AWS, Azure, or GCP; Docker; Kubernetes; CI/CD pipelines; and enterprise data architecture.
- Strong stakeholder communication skills and the ability to translate business needs into technical requirements and lead workshops.
- Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Data Science, Machine Learning, or a related field.
- Preferred: a master’s degree, experience in insurance or other regulated industries, speech-to-text, OCR, image captioning, published research, patents, open-source contributions, advanced MLOps and AI governance expertise, or relevant certifications such as AWS ML Specialty, Azure Data Scientist, GCP Professional ML Engineer, or TOGAF.
Benefits
- Professional development opportunities, interesting work, and supportive leadership.
- Inclusive and flexible work environment with opportunities to create solutions and have impact for colleagues, clients, and communities.
- Range of career opportunities, benefits, and rewards supporting employee well-being.
- Hybrid work based in Singapore, with at least three days per week in the local office or working onsite with clients; office-based teams also have at least one weekly anchor day.
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