Forward Deployed Engineer

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 stronger prompts, agent workflows, golden tasks, adversarial cases, regression suites, KB rules, SOPs, and release gates. Every engagement should improve the delivery system—not merely complete the immediate request.

• Agent governance

  Define what each agent may access, decide, change, test, communicate, or release autonomously. Maintain least-privilege access, healthcare-data boundaries, auditable intervention points, and mandatory approval gates for security, DPO, financial, client-facing, and production-impacting actions. Agent actions must be traceable, reversible, and evidence-backed.

What success looks like

Success means operating as an AI-native, business-facing Forward Deployed Engineer who turns ambiguous client and commercial problems into working, measurable outcomes.

Own the path from client problem to measurable adoption: establish the baseline, define the value hypothesis, agree the success measure and decision owner, validate the workflow with real users, and determine whether to scale, revise, or stop. Support rollout, operational enablement, incident communication, and post-release adoption—not merely technical handover.

Our Benefits

• Make a Real Impact: Opportunity to contribute to a leading digital health company's rapid growth.

• Fast-paced Start-up Environment: Experience an environment where you get to own and make tangible impact without bureaucracy getting in the way of rapid decision-making.

• Great Team: Collaborate with intelligent, friendly, and supportive professionals from diverse backgrounds.

• Hands-on Learning & Growth: Gain hands-on experience in strategy, partnerships, operations, and product innovation within a growing industry.

• Competitive Compensation & Benefits: Competitive compensation and performance-based bonus. Holistic health insurance for your peace of mind for both in-patient and out-patient coverage.

How to apply

If you believe you have what it takes for this role, click ‘Apply’ and  join us on our journey to make a positive impact on the lives of people through innovative healthcare solutions!

What we are looking for

Required:

• AI-native by default. Advanced, hands-on experience using AI agents as the primary delivery layer across intake, repo mapping, solution design, implementation, QA, security review, release, and remediation—not merely as autocomplete.

• Agent orchestration expertise. Able to design and operate specialised agents for backend, frontend, QA, security/DPO, release, and healthcare workflows such as eligibility, claims, pharmacy, and payments.

• Strong systems and production judgement. Able to direct agents across unfamiliar repositories, APIs, databases, integrations, CI pipelines, and logs while controlling scope and assessing production risk. This is not a conventional manual engineering role.

• Strong AI SDLC expertise. Experienced with prompt and version control, agent observability, rollout and rollback controls, RBAC, consent, and incident management.

• Strong eval discipline. Able to build golden tasks, adversarial and trap cases, rubrics, pass/fail gates, regression suites, and remediation or training loops.

• Strong knowledge-base discipline. Turns failures and validated learnings into durable rules, checklists, SOPs, agent instructions, and quality gates.

• Evidence-led QA. Defines edge cases upfront, runs exact verification steps, preserves outputs, and produces reproducible evidence others can trust.

• Sound healthcare data judgement. Understands the boundaries around PII, PHI, NRIC, clinical, claims, and insurer data, including when AI use is prohibited or requires DPO or security escalation.

• Strong healthcare workflow reasoning. Can reason across payer rules, eligibility, benefits, claims, pharmacy fulfilment, provider and TPA operations, payment states, exceptions, consent, and audit trails.

• Relevant healthcare domain capability. Able to operate effectively in healthtech, insurtech, payer/provider platforms, claims, pharmacy fulfilment, benefits administration, or telehealth.

• Effective in high-ambiguity environments. Challenges vague requirements, surfaces cross-functional trade-offs, and reaches the right answer through structured building, testing, and evaluation.

• Evidence-based communication. Clearly separates facts, assumptions, risks, decisions, and unresolved gaps—and never invents approvals, results, evidence, or production readiness.

This role suits builders who own outcomes end-to-end and may not be a fit for those who only write requirements, or who treat AI tools as autocomplete without owning the output.