Job Opportunities API

Temus is a Temasek-backed consulting firm providing digital transformation solutions for the private and public sectors. W e aspire to be a strategic partner in realising the Singapore Government’s Smart Nation vision. We are headquartered in Singapore and have more than 400 employees across a wide range of disciplines in strategy, design, architecture, technology, data & AI. Role Overview The Snowflake Delivery Lead is responsible for leading the enterprise delivery of AI-powered data and analytics solutions built on Snowflake. This is a cross-functional role that combines AI product leadership, data strategy, semantic architecture, enterprise stakeholder management, delivery governance, and production readiness. The role is accountable for ensuring that AI agents and analytics products are not only technically functional, but accurate, explainable, scalable, trusted, and aligned with enterprise decision-making needs. The role acts as the senior bridge between business leadership, technology teams, data engineers, AI engineers, and platform partners. It requires the ability to define product direction, challenge technical assumptions, structure testing methodology, manage release risk, and provide executive-level confidence that the solution is fit for production use. Key Responsibilities 1. Enterprise AI Data Product Leadership Lead the end-to-end development, validation, and deployment of AI-powered analytics products on Snowflake. Own the overall product direction, operating model, delivery approach, and quality bar for AI-enabled data solutions. Translate senior stakeholder expectations into product priorities, semantic layer requirements, agent behavior standards, test coverage, and release criteria. Ensure the solution supports real enterprise decision-making, not just technical experimentation. Define what “good” looks like across answer accuracy, explainability, consistency, usability, governance, and production resilience. Act as the senior product owner for AI analytics use cases, balancing business value, technical feasibility, risk, and delivery timelines. 2. AI Agent Evaluation, Testing Strategy and Quality Assurance Design and oversee the testing strategy for AI agents, including manual evaluation, automated regression testing, question bank design, scenario coverage, variation testing, and trace review. Lead the evaluation of AI outputs by checking not only final answers, but also the reasoning process, tool usage, filtering logic, assumptions, and data retrieval path. Distinguish between true answer mismatches, agent instability, data issues, semantic design gaps, instruction failures, and user ambiguity. Ensure that fixes are validated through repeat testing and variation questions before release. 3. Root Cause Diagnosis and Remediation Leadership Lead root cause analysis for incorrect, unstable, or regressed AI agent responses. Classify issues across semantic model defects, data pipeline issues, agent instruction gaps, tool-selection errors, filter logic problems, identifier resolution failures, caching issues, permissions gaps, and answer-formatting weaknesses. Decide whether remediation should happen in the semantic layer, agent instructions, data transformation logic, orchestration layer, application layer, or user experience. Guide developers on the practical implications of each fix and ensure remediation improves system reliability without introducing new regressions. Maintain a clear view of open defects, severity, business impact, ownership, and production-readiness implications. 4. Executive Stakeholder and Partner Management Serve as the interface between client leadership, business users, technology teams, Snowflake teams, engineering teams, testers, and project sponsors. Translate technical findings into executive-level implications, including business risk, delivery impact, production readiness, and rec