Principal Developer Solution Architect (Healthcare EMR/EHR, Ophthalmology)

Job Description

Job Description

Principal Developer – Solution Architect (Healthcare EMR/EHR, Ophthalmology)

Location: Remote (India)
Type of Role: Full Time
Working hours - UK Shift

An organization is seeking a hands-on Principal Developer / Solution Architect to help design and build technical solutions at the intersection of healthcare EMR/EHR systems and ophthalmology-specific workflows. This role sits at a tech lead / solution architect level but must remain fully hands-on — actively writing code, architecting solutions, and directly contributing to development on a day-to-day basis, using AI development tools and LLM-powered workflows to enhance speed and quality. This is not an oversight-only or purely advisory position; candidates who have moved away from hands-on coding into management-only roles are not a fit. The ideal candidate has direct, hands-on experience with OpenEMR, strong AI/LLM fluency, and understands how ophthalmology practices and EHR/EMR systems integrate, including ophthalmology-specific clinical workflows and data structures.

• Architect and personally build technical solutions integrating with EMR/EHR systems, with a focus on ophthalmology workflows.

• Work directly, hands-on, with OpenEMR (or similar open-source/commercial EMR platforms), including configuration, customization, and integration.

• Design system architecture while remaining actively hands-on in implementation and coding — not delegating core development work.

• Write and review code, contributing directly to the codebase alongside architectural decision-making.

• Use AI development tools and coding assistants (e.g., Claude Code or similar) as part of the day-to-day development workflow to accelerate coding, debugging, and architecture work.

• Evaluate and integrate LLM-powered features or capabilities into the platform where relevant to ophthalmology triage, diagnosis support, or clinical workflows.

• Collaborate with product, clinical, and engineering stakeholders to translate healthcare and ophthalmology-specific requirements into technical solutions.

• Guide technical direction and best practices at a tech lead level, while remaining a primary hands-on contributor.

• Ensure EMR/EHR integrations meet healthcare data standards and interoperability requirements.

• Troubleshoot and resolve complex technical issues directly, through hands-on debugging and problem-solving.

• Document architecture decisions, integration patterns, and technical designs.

• Experience at a Solution Architect level, or one level below (e.g., Principal Developer / Tech Lead) — must be actively hands-on in coding and development, not solely in an oversight or advisory capacity.

• Direct, hands-on experience with OpenEMR, including real implementation and integration work.

• Hands-on experience working with EMR/EHR systems in a healthcare setting.

• Hands-on experience with ophthalmology-related EMR/EHR workflows or integrations.

• Strong software architecture and system design skills, combined with a proven, current track record of writing production code.

• Hands-on experience using AI development tools (e.g., Claude Code or similar) in a real development workflow.

• Practical experience working with LLMs — integrating LLM APIs, prompt engineering, or building LLM-powered features within a production application.

• Hands-on experience integrating third-party or custom systems with EMR/EHR platforms.

• Strong understanding of healthcare data standards and interoperability considerations.

• Strong communication skills to work with both technical and clinical/product stakeholders.

• Ability to work independently in a remote environment.

• Experience with additional EMR/EHR platforms beyond OpenEMR.

• Familiarity with ophthalmology-specific clinical terminology, diagnostic codes, or exam documentation workflows.

• Experience in a healthcare startup or fast-paced product environment.

• Experience with healthcare interoperability standards (e.g., HL7, FHIR).

• Experience applying LLMs or AI/ML models to clinical decision support, triage, or medical imaging use cases.

• Familiarity with responsible AI practices in healthcare (e.g., bias mitigation, explainability, regulatory considerations for AI in clinical settings).