Job description Senior Software Engineer (SSE) – GLIMS Application Clinisys' AI Philosophy: Building an AI-first organisation is central to Clinisys’ purpose and the impact we deliver. As a global provider of intelligent diagnostic informatics solutions, we build AI-enabled, cloud-based platforms to enhance diagnostic workflows across healthcare, life sciences, and public health. By applying intelligent technology thoughtfully and responsibly, we help laboratories and testing environments operate more effectively, generate meaningful insights at scale, and ultimately support healthier and safer communities. Operating across more than 30 countries, Clinisys expects all colleagues—regardless of role or function—to work confidently with AI-enabled tools, apply digital and analytical thinking, and continuously adapt as technologies evolve, must drive an AI first sense of purpose and urgency. GLIMS Application Context GLIMS (General Laboratory Information Management System) is a healthcare laboratory information system used to support and manage end-to-end laboratory workflows. The application covers a broad functional scope including patient identity and encounter management, order and result management, laboratory analytical workflows, result validation, reporting, and integration with external healthcare systems and instruments. GLIMS is a highly configurable system used across different customers, countries, and laboratory environments, meaning functional behavior varies by configuration, workflows, integrations, and regulations. Operating within a complex healthcare ecosystem, GLIMS quality depends on strong functional understanding, correct handling of healthcare data, robustness across configurations, and ensuring changes do not disrupt critical laboratory processes. Job Summary The Senior Software Engineer (SSE) for the GLIMS application is a strong independent contributor, accountable for technical outcomes (quality, reliability, security, and maintainability) and for proactively surfacing risks, dependencies, and trade-offs to ensure safe delivery in a regulated healthcare environment. The SSE practices an AI-first engineering approach—using AI as a default accelerator across the SDLC (analysis, design, implementation, testing, documentation, troubleshooting, and continuous improvement) while retaining full human accountability for correctness, quality, privacy, and compliance. The SSE is responsible for the end-to-end lifecycle of complex features and improvements— from analysis and design through implementation, testing, deployment support, and ongoing maintenance. Working in a regulated healthcare context, the SSE delivers reliable, secure, maintainable software across highly configurable customer environments, collaborates closely with Product, Requirements Engineering, QA, Documentation, and Support, and helps raise engineering quality through design/code reviews, mentoring, and continuous improvement. Tasks & Responsibilities Software development (end-to-end ownership): Independently deliver complex GLIMS features/fixes across the full lifecycle (analysis, design, implementation, unit and integration testing, and maintenance) and take responsibility for outcomes in highly configurable customer environments. AI-first delivery: Identify opportunities where AI can improve engineering productivity and add value in GLIMS; work with Product/Requirements to assess feasibility, risk, and compliance; and use AI-assisted tools (GitHub Copilot preferred) responsibly while maintaining quality and security. SDLC and quality-system ownership: Execute and lead work within a defined, quality-managed SDLC by producing/maintaining appropriate design and technical documentation, ensuring requirements traceability, following change control, and meeting verification/validation evidence expectations unit/integration tests, QA validation) before release. (code reviews, AI-assisted engineering artifacts: Use AI to draft and improve engineering artifacts (e.g., technical designs, test cases, edge-case checklists, troubleshooting hypotheses, and release notes), ensuring appropriate review and validation before content is relied upon or becomes part of the controlled record. Translate functional requirements/specifications into robust technical designs and well-structured, tested code; identify design gaps early and propose pragmatic solutions. Perform accurate technical analysis and effort estimation; proactively communicate risks, dependencies, and trade-offs to help keep delivery on track. Technical leadership for a subsystem/area: Own and drive the technical direction for a GLIMS subsystem/functional area; make and document key technical decisions, align stakeholders (Product, Requirements, QA, Support, and adjacent engineering teams) on trade-offs and approach, and ensure solutions are coherent across components and configurations. Architecture, quality, and maintainability: Improve code structure and component design in service of testability, maintainability, and long-term evolution; contribute to modernization efforts and adoption of new technologies where appropriate. Performance and reliability: Design with a performance and robustness mindset; review code for reusability, correctness, and potential performance issues; help ensure changes do not disrupt critical laboratory workflows. Support and operational ownership: Provide guidance and 3rd line support for the product area(s) you own; lead/drive complex incident investigations and root-cause analysis, propose and implement fixes, and help reduce repeat incidents through preventive improvements. Collaboration and stakeholder engagement: Collaborate closely with Product Management, Requirements Engineering, QA, Documentation, Support/Services and adjacent engineering teams; participate in customer discussions as needed to ensure solutions meet real-world needs. Mentoring and technical guidance: Mentor junior and mid-level engineers through technical guidance, pairing, and constructive code/design reviews; help unblock others, share domain knowledge, and raise the team’s engineering standards so delivery is not dependent on single points of failure. Knowledge / Skills / Abilities Excellent verbal and written communication skills; able to communicate clearly with technical and non-technical audiences and align stakeholders on trade-offs, risks, and delivery plans. Strong AI-assisted development capabilities (AI-first mindset): can spot and propose high-value AI opportunities for productivity and/or GLIMS functionality, and apply AI tools responsibly with attention to quality, security, privacy, and regulatory constraints. Responsible AI use in regulated environments: Demonstrates strong AI literacy and sets the bar for responsible use of AI-assisted tools (prompt hygiene, data handling—no sensitive customer/PHI in prompts—plus licensing/IP awareness), ensuring outputs are validated and compliant before use. Preferred: hands-on experience with GitHub Copilot in VSCode: day-to-day engineering tasks including code review support, unit test creation, refactoring assistance, and documentation generation. Software design and problem solving: Strong software design and problem-solving skills; able to break down complex problems and propose multiple solution options based on sound analytical judgment. Delivery ownership: Proven ability to independently own delivery of features/projects, including design, implementation, unit/integration testing, and deployment readiness/support. Maintainability and documentation: Able to improve code structure/architecture for maintainability and testability; comfortable writing and reviewing technical/design documentation. Quality mindset: High attention to quality: writes clear, concise, well-tested code; applies coding conventions; monitors own work to ensure correctness and reliability. Cross-functional collaboration: Experience collaborating in cross-functional teams (Product, Requirements, QA, Documentation, Support/Services) and contributing effectively in Agile planning and execution. Troubleshooting and operational mindset: Strong troubleshooting skills and operational mindset; able to prioritize unowned/undesirable work that improves team throughput and product stability. Engineering workflows and tooling: Proficiency with modern engineering workflows (source control, branching, pull requests, code reviews, CI/CD where applicable) and tooling used by the team (e.g., Azure DevOps). Important plus: knowledge of Progress OpenEdge ABL (Progress 4GL), including reading/debugging existing code and applying coding conventions. Databases and SQL: Working knowledge of relational database concepts and SQL; ability to reason about data models, performance, and data integrity in healthcare workflows. UI/UX mindset: Ability to design and build reliable business logic and user interfaces with a usability/ergonomics mindset, appropriate to GLIMS user workflows. Regulated environment (quality-managed SDLC): Comfort working in a regulated/quality-managed environment (e.g., defined SDLC, documentation, traceability, verification/validation) and adhering to applicable procedures. Education and Experience Bachelor’s or master’s degree in Computer Science/Engineering (or equivalent through experience). Significant professional experience in software development in an object-oriented environment, including design, implementation, testing, and maintenance of production systems. Experience with relational database management systems and enterprise application development; comfortable diagnosing performance issues across application and database layers. Experience in the health sector (e.g., LIS/LIMS/healthcare software) and/or working with integrations to external healthcare systems and instruments is an asset. Preferred/plus: professional experience with Progress OpenEdge ABL (including debugging, performance analysis, and maintaining large enterprise codebases) is a strong advantage. Any equivalent combination of education and/or experience providing the knowledge/skills/abilities listed above.
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