Head of Engineering

About Ajentik

Ajentik builds agentic AI for healthcare: the systems and workflows that help move information, decisions, and patients through one of the most complex industries in the world. Healthcare is highly regulated, high-stakes, and underserved by modern software, which is precisely where frontier AI can have the most meaningful leverage. The problems are technically interesting, the impact is measurable, and the work matters to the people it touches.

We are a small, focused team. AI is advancing at an unusual pace, and we intend to bring that pace into healthcare with engineering rigour and the care the stakes deserve.

The role

We are hiring our Head of Engineering to lead our technical direction, build alongside the team, and help bring our products into the hands of clinicians, operators, and patients. This is a player-coach role at an early-stage company: you will own architecture, write meaningful code, and grow the engineering function over time.

The scope is broad by necessity in the early days. Over time, the goal is to build a strong engineering team and a durable engineering practice. You will be senior enough to set direction and standards, and stay close enough to the code to keep your judgement sharp.

We do not expect deep expertise across every area listed below. We are looking for strong fundamentals, sound judgement on what to build, buy, or hire for, and the ability to learn quickly in a regulated domain.

Our stack today

You would inherit a real platform, not a blank page, and you are welcome to change it where the reasoning holds:

• Products: healthcare and caregiving applications spanning service-oriented backends, modern web frontends, and cross-platform mobile; managed backend services where they earn their place; and national digital-identity integration for our Singapore products.
• AI in production: agentic features built on frontier large language models, with structured generation, validation-and-retry, and model routing via configuration rather than redeploys; a standard protocol layer connecting models to tools; and the beginnings of an evaluation discipline we want you to turn into a reference point.
• Platform: a managed container-orchestration platform hosted in Singapore, defined entirely as code; declarative, version-controlled deployments; software-defined networking, hardened ingress, and a web application firewall.
• Data and secrets: a replicated relational database with off-site backups and weekly automated restore drills; a dedicated secrets manager as the single source of truth, delivering credentials to workloads and CI through reviewed, automated channels. Application repositories never hold vault or cluster credentials.
• Observability and delivery: full metrics, logging, and tracing coverage; CI pipelines that render and diff every infrastructure change for review; automated dependency updates; and ISO 27001-scoped change management enforced in the repository itself.

What you'll own from day one

• Production reliability, observability, and incident response for AI systems operating in a regulated environment, on the platform described above (healthy and actively invested in, not a rescue project).
• Architecture and technical direction across the platform: backend, web, mobile, and AI.
• Engineering quality: code review, design review, and the team's overall engineering culture.
• Our AI engineering practice (agents, tool use, retrieval, evaluations, and model routing), together with the discipline to ship them safely. Today that means LLM-powered product features and tool integrations live in production; you set where it goes next.
• Co-deciding tooling, infrastructure, and developer-experience investments alongside the founders.
• Hands-on engineering. You will continue to write meaningful code on a regular basis.

What you'll grow into (or hire for) over time

• A small team of strong engineers, with you leading hiring, levelling, and mentorship.
• Compliance-aware infrastructure, extended rather than invented: the platform is in scope for ISO 27001 today, with audit logging, change management, and secrets handling codified in Infrastructure-as-Code. You extend that posture to HIPAA, SOC 2, MOH, and equivalent frameworks as our markets require.
• A repeatable AI shipping pipeline: evaluation harness, prompt and model versioning, safe rollouts, and observability of model behaviour in production.
• Dedicated platform, security, and AI-engineering disciplines as the team grows.
• A healthcare-specific integration surface (clinical data-exchange standards, EHR connectivity, and the broader interoperability layer), adopted where it solves a real problem. Our integration surface today is government identity and payments-adjacent; clinical interoperability is ahead of us, not behind us.
• Other engineering functions that emerge as the company matures.

What success looks like

First 90 days

You understand our stack, our customers, and our risks in depth. Production is in a healthier state than when you arrived. You have made at least one well-reasoned call on technical direction that the founders agreed with, and one they pushed back on; both were productive conversations.

Six months

Engineering operates on the standards you have set. Our AI shipping process (evaluations, rollouts, and observability of model behaviour) is becoming a recognised reference point within healthcare AI. You have made your first engineering hire, or made a considered case for waiting.

Twelve months

Engineering is a real function rather than a few people improvising. We ship faster and more safely than at month one, and the team can take on the company's next level of ambition without being dependent on any single person.

You'll thrive here if you

• Have worked through genuine ambiguity at an early-stage company and have chosen to keep doing so.
• Stay close to the AI frontier by reading research, trying new tools, and forming informed views on what is overhyped and what is underrated.
• Find healthcare's stakes motivating rather than daunting; the regulated, complex, and life-affecting nature of the domain is part of the appeal.
• Hold considered, defensible views on coding agents, evaluation methodology, and what good AI engineering looks like today.
• Care about developer experience as much as user experience, and have invested in both.
• Can move between architecture, code, and team leadership in the same week while maintaining quality across all three.
• Are willing to try approaches that may not work, wind them down thoughtfully when they do not, and capture what you learned.

Nice to have

• Prior founding-engineer, head-of-engineering, or early-stage technical leadership experience.
• Background in healthcare, life sciences, or another regulated industry (ISO 27001, HIPAA, SOC 2, HITRUST, MOH, or equivalent local frameworks).
• Hands-on depth in any of container platforms and declarative operations, high-scale backend services, cross-platform mobile, or LLM-application engineering; we do not expect all of them.
• Open-source contributions in AI, developer tooling, or infrastructure.
• Experience designing AI evaluation systems, agent frameworks, or safety and observability tooling.
• Public technical writing, talks, or side projects that demonstrate how you think.

How we work

• A small team with minimal bureaucracy.
• We move quickly when decisions need to be made, and we communicate clearly when they take more time.
• "Disagree and commit" is our default; thoughtful objections are welcomed and engaged with seriously.
• Progress is measured by shipped value and safe outcomes rather than time spent at a desk.
• Our infrastructure is code, our changes are reviewed, and our compliance posture is part of the engineering system rather than a binder on a shelf. You will find the receipts in the repository.