Job Opportunities API

About WhiteCoat WhiteCoat is a Singapore-headquartered omnichannel provider of integrated health and wellness services that serves as the first and single touchpoint for all care needs in Southeast Asia. Since launching in 2018, WhiteCoat’s digital platform powers a wide range of services including tele- and in-person consultations, as well as medication fulfilment and diagnostic testing, across primary, specialist and allied care. With a focus on the B2B space, WhiteCoat has forged strategic partnerships with the region’s leading insurers, corporates and care providers, to provide accessible and affordable high-quality care to its users. The Group currently has offices in Singapore, Indonesia, Malaysia and Vietnam. For more information on WhiteCoat, please visit https://whitecoat.global .   What you will be doing We are seeking a Forward Deployed Engineer who can sit close to real business and healthcare workflows, clarify ambiguous requirements, design the solution, build working software, produce tests, generate QA evidence, and maintain the AI/agent/KB systems that make delivery faster and safer. This is a client-facing, AI-native delivery role. You will embed with client and business teams, convert ambiguous operational problems into agent-executable work, and operate specialised AI agents as the default delivery workforce. You are not joining to manually process an engineering backlog. You are joining to design, govern, and continuously improve an AI-native delivery system that takes client problems from discovery through adoption and measurable production outcomes. You remain accountable for every decision, claim, release, and business result produced through that system. On a day-to-day basis, you will be responsible for Business and client discovery Work directly with clients and commercial, product, operations, QA, security, DPO, and platform stakeholders. Turn vague asks into precise, agent-ready briefs covering the business objective, affected users and systems, workflows, market rules, data classifications, expected value, acceptance criteria, risks, blockers, and decision owners. AI-led solution design Direct agents to map repositories, systems, APIs, data flows, permissions, audit requirements, exception paths, monitoring, and rollout and rollback controls. Decide whether the right answer is configuration, a workflow change, an integration, a prototype, a client-specific exception, a production feature, or a reusable platform capability. Agent-orchestrated delivery Decompose business outcomes into agent-owned workstreams, assign the appropriate context, tools, permissions, and acceptance criteria, and manage dependencies and handoffs between agents. Reconcile conflicting outputs and intervene when risk, novelty, or agent limitations require human judgement. Evals, QA, and release evidence Define edge cases and pass/fail criteria before delivery. Require agents to execute functional, negative, regression, API and contract, market-specific, permission, privacy, and healthcare-workflow tests. Suggested tests do not count—only executed tests with reproducible evidence support a release decision. Healthcare data and production risk Ensure agents operate within approved boundaries for PII, PHI, NRIC, clinical, claims, insurer, pharmacy, and payment data. Recognise when data must not enter an AI system and when security or DPO escalation is mandatory. Client-facing commercial execution Explain options, constraints, risks, and trade-offs clearly to technical and non-technical stakeholders. Challenge requirements that are vague, unsafe, low-value, or unnecessarily bespoke. Keep delivery tied to business value and never invent approvals, test results, security clearance, or production readiness. Agent and knowledge-system improvement Turn defects, UAT failures, unsafe assumptions, and delivery friction into s