Principal Software Architect - Next Gen Informatics

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 p...