About this role
Job Requirements
Job Description
Principal Software Architect is responsible for defining and evolving the end-to-end architecture of high performant, scalable, resilient next-generation laboratory informatics software platform, including instrument connectivity, laboratory workflows, quality control (QC), result management, and enterprise integrations. The role requires strong software engineering and architectural expertise with demonstrated experience in AI-native software development - requiring the ability to transform conventional engineering workflows into AI-enabled development practices using coding agents, automated code analysis, agentic workflows, AI-assisted testing, and continuous verification while maintaining architectural integrity, security, and product quality.
Key Responsibilities
Enterprise and Solution Architecture
Own the end-to-end software architecture, including system boundaries, component responsibilities, interfaces, data flows, deployment topology, and technology choices.
Define modular, extensible architectures supporting multiple instrument vendors, laboratory sites, product configurations, and deployment environments.
Establish architectural principles, design patterns, interface contracts, coding standards, and reference architectures.
Make informed build-versus-buy and architectural trade-off decisions considering reliability, maintainability, performance, security, and total cost of ownership.
Identify architectural risks, structural technical debt, scalability bottlenecks, and opportunities for platform simplification.
Guide architecture evolution and modernization of legacy systems without compromising existing customer workflows or data integrity.
AI-Native Software Architecture and Engineering
Define the technical strategy for integrating AI-native development practices across requirements analysis, architecture, implementation, testing, code review, and maintenance.
Design AI-enabled engineering workflows using coding agents, agentic development environments, codebase intelligence, and automated verification.
Establish architectural context, repository-level instructions, machine-readable specifications, and guardrails that enable AI agents to work safely within complex software systems.
Evaluate AI agent architectures, tool calling, context management, retrieval-augmented generation (RAG), Model Context Protocol (MCP), and code graph technologies where appropriate.
Establish repeatable evaluations for AI-generated code, including correctness, architectural conformance, test effectiveness, security, maintainability, and regression risk.
Drive measurable improvements in engineering productivity without compromising software quality or release integrity.
Instrument Connectivity and Interoperability Architecture
Define a reusable connectivity architecture for integrating diagnostic instruments from multiple vendors.
Establish abstractions for protocol adapters, device drivers, message parsing, normalization, acknowledgements, retry, buffering, and recovery.
Guide implementation of TCP/IP and serial communication, vendor-specific protocols, and relevant standards such as CLSI LIS01/LIS02 and HL7 v2.
Defining canonical data models and interface contracts for instrument messages, results, QC data, device status, and configuration.
Design for intermittent connectivity, duplicate or malformed messages, out-of-order events, service restarts, and safe recovery.
Establish simulator-based testing and conformance strategies that reduce the cost and complexity of onboarding new instruments.
Laboratory Domain and Data Architecture
Translate laboratory workflows and domain requirements into explicit, testable architectural models.
Define architectures for QC management, Levey–Jennings charts, Westgard rules, result review, QC closeout, alerting, and audit trails.
Establish data models and persistence strategies for historical results, QC observations, configuration versions, rule changes, and operator actions.
Ensure deterministic processing, traceability, historical reproducibility, and appropriate handling of concurrent updates.
Key Technical Skills and Experience
Software Engineering
15+ years in software engineering, including significant experience in software architecture and technical leadership
Strong proficiency in C#, .NET / .NET Core, object-oriented programming, SOLID principles, and software design patterns.
Experience with distributed systems, asynchronous programming, REST APIs, SQL databases, and fault-tolerant high-performance scalable applications.
Experience with Angular, TypeScript, or comparable frontend frameworks.
Strong understanding of networking fundamentals, automated testing, CI/CD, debugging, security, and production issue resolution.
Domain Experience - Mandatory
Deep expertise in laboratory middleware systems, instrument drivers, message translation.
Clinical laboratory workflows, QC processes, result review, and instrument integration.
Laboratory instrument communication standards, including ASTM, CLSI LIS01/LIS02 and HL7 v2.
Healthcare IT, LIS, laboratory middleware, diagnostic software, or IVD product development.
Applicable healthcare software lifecycle, quality-management, risk-management, and cybersecurity standards.
AI-Native Development - Mandatory
Demonstrated hands-on experience using AI coding agents and agentic development tools in real software delivery projects.
Ability to design and implement AI-enabled engineering workflows spanning requirements analysis, codebase exploration, implementation, code review, testing, debugging, and documentation.
Practical experience with tools such as GitHub Copilot, Claude Code, Cursor, or equivalent agentic development environments.
Experience creating and maintaining repository-level AI instructions, reusable agent workflows, project context, and engineering guardrails.
Understanding of agent tool use, context windows, structured outputs, model limitations, and techniques for grounding AI-generated changes in actual code and specifications.
Experience integrating AI agents with source control, work items, build pipelines, test frameworks, and other engineering tools.
Ability to establish automated verification & human review mechanisms for AI-generated code.
Demonstrated understanding of AI failure modes, including hallucinated APIs, incorrect assumptions, insecure code, incomplete test coverage, and unintended architectural changes.
Practical knowledge of secure AI usage, confidential-data protection, access controls, auditability, and software licensing considerations.
Experience building custom coding agents, agentic workflows, or multi-agent engineering systems.
Familiarity with retrieval-augmented generation (RAG), embeddings, vector search, and codebase indexing.
Experience with Model Context Protocol (MCP), tool integrations, or equivalent mechanisms for exposing engineering systems to AI agents.
Experience with code graphs, dependency analysis, semantic code search, and repository-scale reasoning.
Qualifications and experience
Engineering degree in Computer Engineering, or a related discipline.
15+ years of professional experience in SW product development
Sound knowledge of SDLC and agile practices.
Other Attributes
Excellent spoken & written communication skills
Detail oriented with strong problem solving and analytical skills
Team player with a proactive approach
Self-starter, quick learner, and passionate problem resolver
Benefits
Job Description
Principal Software Architect is responsible for defining and evolving the end-to-end architecture of high performant, scalable, resilient next-generation laboratory informatics software platform, including instrument connectivity, laboratory workflows, quality control (QC), result management, and enterprise integrations. The role requires strong software engineering and architectural expertise with demonstrated experience in AI-native software development - requiring the ability to transform conventional engineering workflows into AI-enabled development practices using coding agents, automated code analysis, agentic workflows, AI-assisted testing, and continuous verification while maintaining architectural integrity, security, and product quality.
Key Responsibilities
Enterprise and Solution Architecture
Own the end-to-end software architecture, including system boundaries, component responsibilities, interfaces, data flows, deployment topology, and technology choices.
Define modular, extensible architectures supporting multiple instrument vendors, laboratory sites, product configurations, and deployment environments.
Establish architectural principles, design patterns, interface contracts, coding standards, and reference architectures.
Make informed build-versus-buy and architectural trade-off decisions considering reliability, maintainability, performance, security, and total cost of ownership.
Identify architectural risks, structural technical debt, scalability bottlenecks, and opportunities for platform simplification.
Guide architecture evolution and modernization of legacy systems without compromising existing customer workflows or data integrity.
AI-Native Software Architecture and Engineering
Define the technical strategy for integrating AI-native development practices across requirements analysis, architecture, implementation, testing, code review, and maintenance.
Design AI-enabled engineering workflows using coding agents, agentic development environments, codebase intelligence, and automated verification.
Establish architectural context, repository-level instructions, machine-readable specifications, and guardrails that enable AI agents to work safely within complex software systems.
Evaluate AI agent architectures, tool calling, context management, retrieval-augmented generation (RAG), Model Context Protocol (MCP), and code graph technologies where appropriate.
Establish repeatable evaluations for AI-generated code, including correctness, architectural conformance, test effectiveness, security, maintainability, and regression risk.
Drive measurable improvements in engineering productivity without compromising software quality or release integrity.
Instrument Connectivity and Interoperability Architecture
Define a reusable connectivity architecture for integrating diagnostic instruments from multiple vendors.
Establish abstractions for protocol adapters, device drivers, message parsing, normalization, acknowledgements, retry, buffering, and recovery.
Guide implementation of TCP/IP and serial communication, vendor-specific protocols, and relevant standards such as CLSI LIS01/LIS02 and HL7 v2.
Defining canonical data models and interface contracts for instrument messages, results, QC data, device status, and configuration.
Design for intermittent connectivity, duplicate or malformed messages, out-of-order events, service restarts, and safe recovery.
Establish simulator-based testing and conformance strategies that reduce the cost and complexity of onboarding new instruments.
Laboratory Domain and Data Architecture
Translate laboratory workflows and domain requirements into explicit, testable architectural models.
Define architectures for QC management, Levey–Jennings charts, Westgard rules, result review, QC closeout, alerting, and audit trails.
Establish data models and persistence strategies for historical results, QC observations, configuration versions, rule changes, and operator actions.
Ensure deterministic processing, traceability, historical reproducibility, and appropriate handling of concurrent updates.
Key Technical Skills and Experience
Software Engineering
15+ years in software engineering, including significant experience in software architecture and technical leadership
Strong proficiency in C#, .NET / .NET Core, object-oriented programming, SOLID principles, and software design patterns.
Experience with distributed systems, asynchronous programming, REST APIs, SQL databases, and fault-tolerant high-performance scalable applications.
Experience with Angular, TypeScript, or comparable frontend frameworks.
Strong understanding of networking fundamentals, automated testing, CI/CD, debugging, security, and production issue resolution.
Domain Experience - Mandatory
Deep expertise in laboratory middleware systems, instrument drivers, message translation.
Clinical laboratory workflows, QC processes, result review, and instrument integration.
Laboratory instrument communication standards, including ASTM, CLSI LIS01/LIS02 and HL7 v2.
Healthcare IT, LIS, laboratory middleware, diagnostic software, or IVD product development.
Applicable healthcare software lifecycle, quality-management, risk-management, and cybersecurity standards.
AI-Native Development - Mandatory
Demonstrated hands-on experience using AI coding agents and agentic development tools in real software delivery projects.
Ability to design and implement AI-enabled engineering workflows spanning requirements analysis, codebase exploration, implementation, code review, testing, debugging, and documentation.
Practical experience with tools such as GitHub Copilot, Claude Code, Cursor, or equivalent agentic development environments.
Experience creating and maintaining repository-level AI instructions, reusable agent workflows, project context, and engineering guardrails.
Understanding of agent tool use, context windows, structured outputs, model limitations, and techniques for grounding AI-generated changes in actual code and specifications.
Experience integrating AI agents with source control, work items, build pipelines, test frameworks, and other engineering tools.
Ability to establish automated verification & human review mechanisms for AI-generated code.
Demonstrated understanding of AI failure modes, including hallucinated APIs, incorrect assumptions, insecure code, incomplete test coverage, and unintended architectural changes.
Practical knowledge of secure AI usage, confidential-data protection, access controls, auditability, and software licensing considerations.
Experience building custom coding agents, agentic workflows, or multi-agent engineering systems.
Familiarity with retrieval-augmented generation (RAG), embeddings, vector search, and codebase indexing.
Experience with Model Context Protocol (MCP), tool integrations, or equivalent mechanisms for exposing engineering systems to AI agents.
Experience with code graphs, dependency analysis, semantic code search, and repository-scale reasoning.
Qualifications and experience
Engineering degree in Computer Engineering, or a related discipline.
15+ years of professional experience in SW product development
Sound knowledge of SDLC and agile practices.
Other Attributes
Excellent spoken & written communication skills
Detail oriented with strong problem solving and analytical skills
Team player with a proactive approach
Self-starter, quick learner, and passionate problem resolver