Full-Stack AI Product Engineer
Cheil USA
- Mississauga, Ontario, Canada
- Hybrid
- C$85,000 - C$90,000 a year
- Posted Jul 13, 2026
Job description
About the role
The Full‑Stack AI Product Engineer is a newly created, hands‑on role responsible for designing, building and scaling AI‑powered products, internal tools, SaaS‑like platforms and automation systems. The engineer works across the full stack—from front‑end UI to back‑end services—integrating LLM APIs, data sources and cloud services to deliver MVPs and production‑ready solutions that address business challenges.
About the company
Cheil USA (Cheil Worldwide) is a global marketing agency that creates connected experiences for brands, serving as the lead agency for Samsung in Canada and working with other globally recognized brands. Powered by a team of 60+ creative problem‑solvers, the company blends data, insights, creativity and technology to deliver culturally relevant, experiential and data‑driven marketing.
Requirements
- 4+ years of professional software development experience.
- Strong full-stack development experience.
- Proven experience building web applications from front-end to back-end.
- Hands-on AI development experience is required.
- Experience building AI-powered applications, assistants, agents, RAG systems, or LLM-integrated tools.
- Experience working with LLM APIs such as OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, or similar.
- Experience with React, Next.js, TypeScript, JavaScript, or similar front-end technologies.
- Experience with Python and/or Node.js.
- Experience building APIs, back-end services, databases, and integrations.
- Experience with authentication, permissions, secure application design, and data handling.
- Experience deploying applications to cloud or managed hosting environments.
- Ability to work independently and turn ambiguous business needs into working products.
- Strong communication, documentation, and problem-solving skills.
- Experience with AI agent frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Google ADK, or similar.
- Experience with vector databases such as Pinecone, Weaviate, Chroma, Qdrant, pgvector, or Elasticsearch.
- Experience with cloud platforms such as AWS, Azure, GCP, Vercel, Firebase, Supabase, or similar.
- Experience building SaaS products, internal enterprise tools, dashboards, or workflow automation platforms.
- Experience with marketing technology, CRM, e-commerce, CMS, analytics, personalization, or data platforms.
- Experience with AI evaluation, monitoring, cost optimization, and responsible AI practices.
- Experience building products for non-technical business users.