Full-stack Software Engineer

Full-Stack Software Engineer — AI-Native Development

Location: Singapore, Hybrid

Level: Mid to Senior.

Type: Full-time. Probable relocation to Johor Bahru/Singapore after first 6 months, depending on performance.

About AIGP Health

AIGP Health is a physician-led health-tech company building Anzu, an agentic clinical intelligence platform supporting care before, during and after every encounter. Beyond scribing, Anzu synthesizes records, supports guideline-aware decisions, detects care gaps, automates follow-up and enables measurable value-based care. Built by doctors for doctors, we reduce preventable harm, return time to clinicians and extend high-quality care to more patients.

About the Role

We're hiring a Full-Stack Software Engineer to help design, build, and ship features across our HSA approved digital health product, Anzu — from backend services and data/ML infrastructure to the frontend experiences our users and physicians touch every day, with quality baked in through rigorous QA/QC rather than bolted on afterward. This role is built around an AI-Driven Development Life Cycle (AIDLC): you'll use Claude Code and similar agentic coding tools as first-class parts of how you plan, build, test, review, and ship software. We're looking for someone who thinks about how to prompt, scaffold, and supervise AI coding agents as a core engineering skill, on par with writing code by hand.

What You'll Do

AI-Native Development Workflow (AIDLC)

• Use Claude Code (and comparable agentic coding tools) daily across the full development lifecycle: spec'ing, scaffolding, implementing, testing, debugging, and documenting.
• Design workflows, prompts, and guardrails (custom commands, skills, subagents, CI hooks) that let AI agents do more of the repetitive and boilerplate work safely and reliably.
• Review AI-generated code and AI-driven test coverage with the same rigor as human-written code — you own correctness, security, and maintainability regardless of who (or what) wrote the first draft.
• Help evolve our team's AIDLC practices: what to automate, what needs a human in the loop, and how to measure whether AI-assisted workflows are actually improving speed and quality.

Backend

• Design and build backend services, APIs, and data pipelines in Python, with an emphasis on correctness, performance, and observability.
• Work with ML/data-heavy components (e.g., PyTorch-based services or pipelines) where relevant to product features.
• Build and operate cloud infrastructure on AWS and/or GCP — deployments, storage, data pipelines, and monitoring.
• Own data models and integrations that must hold up to digital health-grade accuracy, security, and auditability requirements.

Frontend

• Build responsive, accessible, well-tested user interfaces that make complex data and workflows easy to use for clinical, research, or operational end users.
• Collaborate with product and design to turn requirements into clean, maintainable frontend architecture.
• Optimize for performance and usability, including on data-dense screens (dashboards, tables, visualizations).

QA / QC

• Build and maintain automated test suites (unit, integration, end-to-end) as a first-class deliverable of every feature, not an afterthought.
• Define and enforce quality gates appropriate for health-tech software — including traceability, reproducibility, and defect triage.
• Use AI-assisted testing (AI-generated test cases, mutation testing, fuzzing) to increase coverage and catch edge cases faster than manual QA alone.
• Participate in root-cause analysis for production issues and drive fixes upstream into process and tooling, not just the immediate bug.

What We're Looking For

• 4+ years of professional full-stack software engineering experience, comfortable moving between backend, frontend, and testing/QA work.
• Strong Python skills; experience with PyTorch or other ML/data tooling is a plus, especially if you've shipped data- or model-backed features to production.
• Hands-on experience with cloud platforms — AWS and/or GCP — including deployment, storage, and basic infrastructure-as-code practices.
• Solid frontend fundamentals (modern JavaScript/TypeScript and a component-based framework such as React) and an eye for good UX.
• Real, hands-on experience using Claude Code or a comparable AI coding agent (Cursor, Github Copilot, etc.) in a production codebase — you can speak concretely to what worked, what didn't, and how you supervised the output.
• A QA/QC mindset: you write tests without being asked, think about failure modes proactively, and treat quality as everyone's job.
• Strong written and oral communication — Clearly document specs, AI prompts, and reviews. Comfortable presenting your work in weekly tech and management meetings.
• Comfort with ambiguity and a fast-moving startup environment.

Nice to Have

• Experience in health-tech, biotech, life sciences, or another regulated/high-stakes domain (e.g., fintech, medtech) — you understand what "quality bar" means when the output touches patient or clinical data.
• Familiarity with healthcare data standards and compliance frameworks (e.g., PDPA, HIPAA, GDPR HL7/FHIR, GxP, SOC 2).
• Experience designing custom AI agent workflows and tools
• Prior experience on a small hybrid team where you owned a feature or system end-to-end, from design through production support.

Tech Stack

A snapshot of what you'll work with day to day (expect this to evolve):

• Backend / ML: Python, PyTorch
• Cloud / Infra: AWS, GCP
• Frontend: Modern JavaScript/TypeScript, React (or equivalent component framework)
• AI-Native Tooling: Claude Code, custom agent workflows, CI-integrated AI review and testing