Software Engineer, AI

Role overview

What you’ll do

• Design, build, test, deploy, and support production-ready AI applications; translate requirements into designs, plans, estimates, and acceptance criteria.
• Develop LLM, RAG, semantic search, document/data processing, and agentic solutions; build secure backend services, APIs, integrations, and cloud-native components.
• Optimize ingestion, embeddings, retrieval, prompting, grounding, tool use, memory, and orchestration; evaluate solutions for quality, cost, performance, security, privacy, and operations.
• Productionize solutions with testing, resiliency, observability, monitoring, responsible AI controls, CI/CD, infrastructure-as-code, deployment practices, and MLOps/LLMOps.
• Contribute to reviews, reusable components, engineering standards, technical documentation, and runbooks; collaborate on discovery, design, demonstrations, estimates, and knowledge transfer.

What you’ll bring

• Typically 4–6 years of software engineering experience, including at least 2 years developing AI, machine learning, or generative AI solutions.
• Proven delivery of production-grade applications or AI capabilities beyond proof of concept.
• Strong Python skills and at least one additional language such as TypeScript/JavaScript, Java, C#, or Go.
• Experience with backend services, APIs, integrations, databases, authentication/authorization, and asynchronous or event-driven applications.
• Hands-on experience with Azure, AWS, or Google Cloud and modern AI platforms or models.
• Practical experience with LLM applications, RAG, embeddings, vector search, prompt engineering, AI agents/tool integration, evaluation, and data pipelines.
• Experience with React or Next.js; understanding of software architecture, secure development, responsible AI, and component-level design.
• Experience using AI-assisted engineering tools such as GitHub Copilot, Cursor, or Claude Code; strong communication, estimation, Agile delivery, and customer collaboration skills.

Additional advantageous experience

• Docker, Kubernetes, serverless architectures, messaging, caching, workflow orchestration, and distributed systems are nice to have.
• Git, automated testing, CI/CD, infrastructure-as-code, production observability, MLOps/LLMOps, traditional machine learning, model adaptation, data science workflows, scalable AI inference, and relevant certifications are nice to have.

Compensation

• Estimated base pay in Canada is CAD 91,200 to CAD 114,000 annually, plus target incentives.
• Actual salary depends on location, experience, skill set, education, and related certification; ranges may differ by geographic location.

Benefits

• Full-time employees receive medical and dental care, employee and family assistance, retirement savings plan with company matching, life and disability insurance, paid leave, holidays, and an employee discount program.

Workplace and hiring

• The description notes flexibility in planning workdays but does not specify a remote, hybrid, or onsite policy.
• The hiring process includes a criminal record check, education verification, and reference checks; onboarding is remote.