Principal Software Engineer (AI & Data Products)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Software Engineer (AI & Data Products) based in Canada (Remote).

As a Principal Software Engineer, you will provide hands-on technical leadership in designing and building scalable software, data, and AI-powered products supporting critical clinical research workflows. You will drive architectural strategy across product lines, ensuring systems are reliable, secure, maintainable, and aligned with long-term business objectives. Working at the intersection of software engineering, data architecture, and artificial intelligence, you will lead the development of intelligent document processing, information extraction pipelines, and agentic AI applications. You will collaborate with Product, Data Science, Infrastructure, and Engineering teams to deliver high-impact technical initiatives and establish reusable engineering practices. In addition to solving complex architectural challenges, you will mentor senior engineers and influence technical decisions across the organization. This role offers an opportunity to advance healthcare innovation through robust, production-ready AI and data solutions.

Accountabilities:

As a Principal Software Engineer, you will serve as a technical authority responsible for architecture, engineering excellence, and the delivery of sophisticated software and AI solutions. You will translate strategic architectural direction into practical implementations, lead complex cross-functional initiatives, and ensure the long-term performance, scalability, security, and reliability of critical systems.

• Drive technical strategy and architectural decisions across one or more product lines, ensuring alignment with enterprise architecture, engineering standards, and business objectives.

• Own end-to-end product-line architecture, designing scalable, reliable, secure, and maintainable systems that follow broader organizational guidelines.

• Translate high-level architectural strategies into actionable technical designs and implementation plans for engineering teams.

• Lead complex, high-impact technical initiatives involving multiple teams, systems, and product components.

• Establish and reinforce engineering best practices, architectural patterns, and design standards to ensure consistency and quality across teams.

• Collaborate with architects to implement and refine technical direction, providing feedback based on practical engineering constraints and production system behavior.

• Resolve highly complex technical challenges involving system scale, ambiguity, performance, and cross-team dependencies.

• Lead architectural and design reviews, evaluating solutions against long-term scalability, performance, reliability, and maintainability requirements.

• Influence technical prioritization and engineering trade-offs, balancing business needs with architectural standards and long-term objectives.

• Mentor and develop senior and staff-level engineers, strengthening technical decision-making, system design capabilities, and engineering excellence.

• Identify and address systemic risks, technical debt, performance bottlenecks, and operational inefficiencies across product lines.

• Drive improvements in system reliability, observability, monitoring, and operational excellence.

• Evaluate and introduce new technologies, ensuring their adoption supports architectural standards and long-term technical strategy.

• Champion application security by implementing security strategies, identifying vulnerabilities, and coordinating remediation efforts based on security scans and penetration testing results.

• Collaborate with Product, Quality Assurance, Support, Cloud, Architecture, Infrastructure, and Data Science teams to ensure cohesive delivery and high-quality outcomes.

• Act as the primary technical authority for assigned product lines, designing transformative solutions with broader organizational impact.

• Architect reliable data pipelines with clearly defined data contracts, schema evolution strategies, end-to-end data lineage, versioning, and idempotent processing.

• Build and productionize document processing and information extraction pipelines, agentic AI applications, and automated evaluation frameworks.

• Establish end-to-end observability across data quality, pipeline reliability, model performance, and inference costs, including longitudinal metrics and automated regression detection.

• Optimize model selection and inference orchestration to balance output quality, latency, throughput, and operational costs.

• Partner with Data Science, Architecture, and Infrastructure teams to develop reusable engineering patterns, shared tools, and clear operational ownership.

Requirements:

The ideal candidate is a highly experienced software engineering leader with extensive expertise in distributed systems, backend technologies, cloud…