The Applied AI Software Engineer is a hands‑on technical role responsible for designing, building, testing, deploying, and supporting secure, reliable artificial intelligence solutions within SE Health. The role translates business, clinical, and operational needs into production‑ready applications using machine learning, generative AI, natural language processing, retrieval‑augmented generation, agentic workflows and orchestration, automation, and related technologies.

Working with product, enterprise architecture, data, security, privacy, clinical, and software engineering partners, the Applied AI Software Engineer delivers solutions across the full development lifecycle, from discovery and prototyping through integration, release, monitoring, and continuous improvement. The role also applies modern agentic software development practices and AI coding agents under human review to increase delivery speed and consistency without compromising quality, security, or accountability. The position applies robust software engineering practices, responsible AI controls, and user‑centred design to ensure AI capabilities are explainable, maintainable, cost‑effective, and aligned with SE Health standards and strategic priorities.

Primary Accountabilities:

AI Solution Design & Development

- Translate business, clinical, and operational requirements into secure, maintainable AI solution designs and technical specifications.
- Develop AI‑powered applications, services, agents, and APIs using Python and TypeScript/Node.js, and approved frameworks, models, and cloud services.
- Build retrieval‑augmented generation solutions, including document ingestion, chunking, embeddings, vector search, grounding, prompt design, and response orchestration.
- Select,



configure, and integrate machine learning and foundation models based on quality, risk, privacy, latency, scalability, and cost requirements.
- Create reusable components, implementation patterns, and technical documentation that accelerate consistent AI delivery.
- Participate in architecture, design, and code reviews and align solutions with enterprise technology standards.
- Design and build multi‑stage large language model pipelines and hybrid retrieval combining vector and lexical search, with section‑aware chunking, re‑ranking, citation‑level source attribution, checkpointing, resumability, and human‑in‑the‑loop approval points.
- Implement structured, schema‑validated model outputs and tool calling, and build document ingestion, generation, redlining, and change‑summary capabilities across Word, PDF, and web sources.

Software Engineering, Integration & Delivery

- Build and maintain data pipelines, model integrations, REST APIs, and application components required to operationalize AI capabilities.
- Apply version control, automated testing, peer review, continuous integration and delivery, containerization, and infrastructure automation practices.
- Integrate AI services with enterprise applications, workflows, identity controls, data platforms, and approved cloud environments.
- Develop unit, integration, regression,



performance, and safety tests; troubleshoot defects and resolve production issues.
- Collaborate in agile delivery with product owners, designers, data professionals, architects, security specialists, quality engineers, and operational teams.
- Build full‑stack application components in TypeScript and Node.js, including React and Next.js or equivalent, as well as Python, backed by relational databases with row‑level security, versioned schema migrations, and immutable audit trails.

Apply agentic software development methodologies and AI coding agents, such as Claude Code, OpenAI Codex, GitHub Copilot, or equivalent, including specification‑driven workflows, autonomous iteration loops, repository‑level agent instruction files, custom commands, skills and hooks, subagent orchestration, Model Context Protocol servers, and tiered automated validation gates, with all agent‑generated work peer reviewed, tested, and traceable in source control.

Responsible AI, Security & Quality

- Embed privacy, security, accessibility, human oversight, transparency, and responsible AI requirements throughout the development lifecycle.
- Design evaluation datasets and automated checks for accuracy, relevance, groundedness, hallucination, bias, toxicity, robustness, and task completion.
- Protect sensitive health and personal information through approved data handling, access controls, logging, encryption, and secure development practices.
- Document model choices, data lineage, limitations, risks, controls, testing evidence, and operational procedures to support governance and auditability.
- Partner with privacy, security, legal, clinical sa

📌 Applied AI Software Engineer (Markham)
🏢 SE Health
📍 Markham