Lead, AI Software Engineer

Strive Health•Quinte West, ON

9h•Hybrid

About The Position

We are building enterprise-grade AI capabilities to operate kidney care services at scale. The Lead AI Software Engineer sits within Strive’s AI Center of Excellence (AI CoE). This role focuses on helping every engineer at Strive design, build, and ship AI-enabled software safely and effectively — from AI-assisted development workflows to agentic and RAG-based applications that support our clinical and business teams. This is not a traditional "lead coder" role. While you will absolutely contribute hands-on to AI-forward applications and pipelines, what differentiates this role is your responsibility to upskill and mentor other engineers, and change the way work gets done. You will be a thought leader, coach, and mentor — the person other engineers look to when they want to understand what an agent is doing, why a context strategy is failing, or how to bend a tool like Claude Code to a hard problem. You will lead by example, working side-by-side with Strivers to replace the mechanical parts of our work with efficient AI tools while protecting the human relationships that make our team and care model special.

Requirements

7+ years of experience in software engineering, data engineering, machine learning engineering, or a closely related field, preferably in healthcare or other regulated technology environments.
2+ years of hands-on experience building and operating AI-powered systems (e.g., generative AI, agentic workflows, RAG applications), beyond surface-level usage of coding assistants.
Internet Connectivity - Min Speeds: 3.8Mbps/3.0Mbps (up/down); Latency < 60 ms.
Ability to travel and be onsite to meet business needs (up to 10% travel).

Nice To Haves

Demonstrated, hands-on experience with intermediate-to-advanced usage of AI-assisted coding tools (such as Claude Code, GitHub Copilot, Cursor, or similar), including configuration, prompt/context strategies, developing your own skills and subagents, and integration into the SDLC.
Strong Python skills with experience building cloud-based data and AI workflows (e.g., microservices, data pipelines, or orchestration using tools like AWS services, Airflow, or Step Functions).
Experience designing and implementing at least one flavor of Retrieval-Augmented Generation (e.g., vector RAG) and working familiarity with alternatives (graph or hybrid RAG), including when to apply each pattern.
Proven experience mentoring and coaching other engineers — providing design, code, and workflow feedback that improves both outcomes and skills over time.
Ability to communicate complex technical and AI concepts clearly to both technical and non-technical stakeholders, including clinicians and business leaders.
Hands-on experience with multiple AI coding assistants and frameworks (e.g., Codex, Cursor, GitHub Copilot, Gemini CLI), and familiarity with emerging spec-driven and agentic frameworks (e.g., GSD, Superpowers, Claude Flow).
Experience designing or consuming MCP servers, custom tools/skills, and multi-agent orchestration patterns in production or pre-production environments.
Strong context engineering instincts — shaping the SDLC (specs, ADRs, architecture diagrams, commit messages, runbooks) so that the why and what of a change are automatically available to AI agents.
Experience building generative or agentic AI applications that interact with real users (internal or external), including UX considerations, guardrails, and monitoring for safety and quality.
Practical experience with vector embeddings, knowledge graphs, or similar techniques for knowledge-base RAG systems, ideally supporting non-technical users such as clinical teams.
Strong AWS background, including experience with foundational services used in data and AI workflows (e.g., Lambda, containers, eventing/queues, data lakes, Redshift, Glue) and infrastructure-as-code (AWS CDK preferred).
Experience working with healthcare data standards (e.g., HL7 FHIR) and clinical stakeholders.
Familiarity with GitLab-based workflows (or transferable GitHub experience), including CI/CD, code review practices, and introducing new tooling or quality gates into an existing SDLC.

Responsibilities

Serve as a senior individual contributor and player-coach in the AI CoE, partnering across Technology and the business to embed AI into how Strive builds products and delivers care-supporting tools.
Help reshape the software development lifecycle itself, moving toward behavior-driven and spec-driven development, expanding the use of skills, agents, and MCP servers, and rethinking the merge request process so that AI-assisted code reviews coach junior engineers as well as critique their work.
Define and help the rest of engineering adopt changes to the software development lifecycle.
Work closely with Product Management, the VP of Engineering, the VP of Product, and the Chief Technology Officer, as well as clinical and business stakeholders, to identify where thoughtful application of AI can produce real gains for the business and for the patients we serve.
Coach and upskill engineers across Strive on AI-assisted coding, agentic workflows, and modern context engineering practices, with a focus on safe, repeatable patterns that teams can adopt independently.
Lead the rollout, configuration, and ongoing maturation of our AI-assisted development tooling (e.g., Claude Code), including experimentation with skills, agents, and MCP servers, and translating findings into best practices.
Help define and evangelize behavior-driven and spec-driven development practices, including AI-readable specs, architecture diagrams, and architecture decision records (ADRs) that make intent and context clear to both humans and AI.
Partner with engineering leadership to introduce AI-assisted code reviews into our GitLab merge request workflow, including patterns that provide coaching feedback for junior engineers while maintaining high quality and safety.
Design and build reference implementations of AI-forward applications — generative, agentic, and RAG-based — that demonstrate what is possible and create reusable patterns, libraries, and templates for other teams.
Collaborate with Product, the VP of Engineering, the VP of Product, the CTO, and other leaders to identify, prioritize, and sequence high-leverage AI opportunities in the product and platform roadmap.
Work closely with security, compliance, and data teams to ensure that AI tools and applications handling PHI and other sensitive data adhere to Strive’s security, privacy, and governance requirements.
Communicate complex AI topics clearly to non-technical audiences (including clinical and business stakeholders and senior executives), translating technical trade-offs into language that supports informed decision-making.
Contribute to standards and documentation for AI usage across engineering, including coding guidelines, prompt and context patterns, agent/skill catalogs, and runbooks for common AI-assisted workflows.
Meet in person with internal stakeholders to facilitate team and business priorities/opportunities, including training sessions, workshops, and demos, as needed.