Senior AI Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Senior AI Engineer Singapore Role Overview This role will be the technical foundation builder for the company's AI transformation. You will design and build the company-wide knowledge infrastructure and context layer that powers future AI applications. This is a highly hands-on role requiring strong backend engineering capability, LLM application experience, product sense, and the ability to operate independently in a fast-moving, ambiguous environment. Role Overview This role will be the technical foundation builder for the company's AI transformation. You will design and build the company-wide knowledge infrastructure and context layer that powers future AI applications. This is a highly hands-on role requiring strong backend engineering capability, LLM application experience, product sense, and the ability to operate independently in a fast-moving, ambiguous environment. Key Responsibilities - Design and build the company-wide AI knowledge infrastructure, including company wiki, internal knowledge base, retrieval layer, and context management system. - Develop scalable LLM application architecture, including RAG pipelines, vector database integration, prompt workflows, API services, monitoring, and deployment. - Own the end-to-end technical delivery of internal AI tools, from backend architecture and basic frontend integration to deployment, testing, and monitoring. - Work closely with business, brand, PR, IR, and leadership stakeholders to translate ambiguous business needs into practical AI systems and technical roadmaps. - Optimize system performance, including token efficiency, latency, caching strategy, retrieval quality, data architecture, and model inference flow. - Evaluate and integrate AI coding tools, LLM frameworks, vector databases, and third-party APIs to improve development efficiency and product quality. - Mentor

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