Design, build, and maintain features across the full stack, owning them from concept through to production and support.
Write clean, well-tested, maintainable code across backend and frontend, and hold a high bar in code review.
Contribute to architecture and technical design decisions, weighing trade-offs across performance, security, cost, and maintainability.
Break down complex requirements into well-scoped, deliverable pieces of work.
Embed AI into everyday engineering practice across coding, code review, test generation, and documentation, wherever it delivers real leverage.
Help define good practice for AI-assisted development, including where it adds value and where human judgement stays in control.
Ensure AI-assisted output is reviewed, secure, and fit for a production healthcare environment.
Measure and share the impact of AI adoption, such as improvements in cycle time, test coverage, or review effort.
Build automated tests at unit, integration, and end-to-end levels as a routine part of delivery.
Diagnose and resolve production issues, using observability and telemetry to reach root cause rather than symptoms.
Partner with product, design, and other engineers to turn requirements into working software.
Communicate technical decisions and trade-offs clearly to both technical and non-technical audiences.
Mentor other engineers and raise the overall capability of the team.
Support Agile ways of working and contribute to improving team processes.
Keep current with modern engineering practice, tooling, and AI developments, and bring ideas back to the team.
Champion secure, maintainable, and pragmatic engineering across the platform.
Features are delivered end to end, well tested, and running reliably in production.
AI is used across the SDLC in a way that measurably improves quality and delivery speed.
AI-assisted output is consistently secure, reviewed, and fit for a regulated environment.
Engineering standards and team capability improve through your contribution.

5 to 6 years of professional software engineering experience building and running production systems.
Demonstrable full stack delivery, comfortable owning a feature across backend, frontend, data, and deployment.
Practical experience introducing or scaling AI tooling within an engineering workflow, not just personal use.
Familiarity with AI coding assistants and agentic tooling (for example GitHub Copilot, Claude Code, Cursor, or similar) and a considered view on where they help and where they do not.
Strong experience in a modern backend language and framework (for example C# / .NET, Node.js, Python, or Go).
Sound API design (REST or GraphQL) and experience with relational and, ideally, caching or NoSQL data stores.
Solid experience with a modern JavaScript framework (React and TypeScript preferred) and an eye for usable, accessible interfaces.
Comfortable with cloud platforms (Azure preferred), CI/CD pipelines, and infrastructure as code.
A pragmatic approach to security, performance, and technical debt.
Excellent communication skills and the ability to collaborate effectively across a wider team.
Experience in healthcare or another regulated industry where data protection and compliance matter.
Exposure to identity and access standards (OAuth2, OIDC, SAML).
Experience with event-driven or real-time systems (for example messaging, WebSockets, or streaming).
Familiarity with observability tooling such as Application Insights or OpenTelemetry.
Contributions to setting AI governance or engineering standards at a team or organisation level.

Thanks for stopping by! We're Doctor Care Anywhere: Doctor Care Anywhere Group plc (DCA) is the UK's largest private provider of telehealth services. The Company works with insurers, healthcare providers and corporate customers to serve patients with a range of digitally enabled telehealth services on its proprietary platform.

1 Doctor Care Anywhere jobs in England & Wales