Software Engineer

Clinisys; Bengaluru Urban; On-site; INR 800,000 - 1,200,000; Full time

Job summary

Clinisys Bengaluru-based software engineer role focuses on building AI-enabled, cloud-based diagnostic informatics platforms. You will develop GLIMS features across the full SDLC, applying AI-assisted tooling with GitHub Copilot while maintaining code quality and regulatory compliance.

You will collaborate with cross-functional teams to translate requirements into reliable software, write tests, conduct reviews, and contribute to maintaining robust laboratory workflows.

Qualifications

• Experience with AI-assisted development tools and workflows.
• Ability to translate functional requirements into technical solutions.
• Strong coding standards, testing, and documentation practices.
• Attention to security, privacy, and regulatory quality requirements.

Responsibilities

• Develop GLIMS features across the software lifecycle (analysis, design, implementation, testing, maintenance).
• Collaborate with Product Management, QA, Documentation, and Support teams.
• Ensure code quality and reviews, maintain traceability and documentation.
• Investigate incidents, deliver reliable fixes, and maintain robust lab workflows.

Skills

AI literacy

GitHub Copilot experience

Code reviews

Problem solving

Communication skills

Progress OpenEdge ABL

SQL

VSCode

Azure DevOps

Tools

VSCode

Azure DevOps

SQL

Job description

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. Software development (delivery with guidance): Develop GLIMS features/fixes across the software development lifecycle (analysis, design, implementation, unit testing, and maintenance), seeking input from more experienced engineers when needed.

• AI-steered development: Work in an AI-steered engineering workflow (GitHub Copilot) where AI is used as the primary accelerator for drafting code, refactoring, and generating unit tests. The engineer remains fully accountable to critically review, validate, and verify all AI-generated output (correctness, security, privacy, performance, and regulatory/quality requirements) before it is merged or released.
• SDLC and quality-system adherence: Execute work in 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 (code reviews, unit/integration tests, QA validation) before release.
• 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.
• AI-driven incident investigation and operational fixes: Leverage a strong AI background and sound engineering practices to investigate complex customer incidents, perform efficient root-cause analysis, and implement reliable fixes for day to day issues.
• Requirements to implementation: Translate functional requirements/specifications into clear technical solutions and well-structured, tested code; raise questions early when requirements or designs are unclear.
• Execution and communication: Execute daily development tasks as assigned and agreed in the team planning process; communicate progress, risks, and deviations in a timely manner.
• Code quality and maintainability: Contribute to code quality and maintainability by following coding conventions, improving readability, adding tests, and participating in code reviews.
• Robustness across configurations: Help ensure changes remain robust across configurations and do not disrupt critical laboratory workflows; collaborate with QA on verification and troubleshooting.
• Product support and prevention: Provide support for assigned product areas (including participation in 3rd line support as needed), investigate issues, propose fixes, and contribute to preventing repeat incidents.
• Stakeholder collaboration: Collaborate closely with stakeholders (Product Management, Requirements Engineering, QA, Documentation, Support/Services and adjacent engineering teams) to ensure solutions meet real-world needs.
• Continuous improvement: Propose improvements to internal procedures and team ways of working to increase efficiency and quality.
• Knowledge / Skills / Abilities Good verbal and written communication skills; able to communicate clearly with technical and non-technical stakeholders.
• Important asset: knowledge of Progress OpenEdge ABL (Progress 4GL), including reading/debugging existing code and applying coding conventions.
• Software engineering fundamentals: Solid software development fundamentals; able to implement moderately complex changes with guidance and learn the codebase effectively.
• AI literacy and responsible use: Strong AI literacy and responsible use of AI assisted tools (GitHub Copilot), including prompt hygiene, data handling (no sensitive customer/PHI in prompts), and licensing/IP awareness.
• GitHub Copilot experience: Hands-on experience with GitHub Copilot in VSCode (e.g., refactoring, unit test generation, and documentation support), combined with strong code review and execution-based validation practices.
• Problem solving: Problem-solving skills: able to break down problems, investigate issues, and propose practical solutions.
• Quality mindset: Writes clear, concise, well-tested code; applies coding conventions; monitors own work to ensure correctness and reliability.
• Engineering workflows and tooling: Comfortable working with Git-based workflows (branching, pull requests, and code reviews) and team tooling (e.g., Azure DevOps), including CI/CD where applicable.
• Data and SQL: Working knowledge of relational database concepts (preferably Progress OpenEdge) and SQL; able to reason about data models, performance, and data integrity.
• Business logic and UI design: Ability to design and build reliable business logic and user interfaces with usability/ergonomics in mind.
• Regulated SDLC experience: Comfort working in a regulated/quality-managed environment, including documentation, traceability, peer review, and adherence to applicable procedures and quality gates.