12 days ago
Montréal, CanadaSenior
Responsibilities
- Develop user-facing interfaces with TypeScript and React and backend services and APIs with Python or Node.
- Build agentic systems involving orchestration, tool and function calling, memory, and guardrails.
- Implement RAG pipelines covering ingestion, chunking, embeddings, vector search, and hybrid search.
- Connect AI systems with enterprise data and applications through APIs, semantic layers, and protocols such as MCP.
- Create evaluation suites and regression tests for LLM-powered features and monitor cost, latency, quality, and production reliability.
- Deploy and maintain AI applications on GCP, Azure, or AWS using containers, serverless infrastructure, and infrastructure-as-code.
- Build and maintain data pipelines across warehouses, lakehouses, and vector stores.
- Use agentic coding tools, communicate with clients and project leads, support pre-sales activities, mentor junior engineers, and contribute to internal accelerators and engineering standards.
Requirements
- 3–5 years of experience in software engineering or data engineering, including extensive hands-on AI tools and LLM-based development over the past year.
- Professional English proficiency is mandatory.
- Strong programming skills in Python and TypeScript/JavaScript, with experience building and consuming APIs.
- Front-end development experience with React or a similar framework and experience with at least one backend framework.
- Hands-on experience with RAG, embeddings, vector search, and at least one agentic framework such as LangGraph/LangChain, Google ADK, Claude Agent SDK, or OpenAI Agents SDK.
- Specialization in at least one major AI platform ecosystem from Google, Anthropic, or OpenAI and working experience with GCP, Azure, or AWS.
- Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.
- Experience building and maintaining data pipelines.
- Bachelor's or master's degree in computer science, engineering, or a related field, or equivalent practical experience.
- An AI platform or cloud certification is required within the first two months if not already held; preferred examples include Claude Certified Developer – Foundations, Google Cloud Professional Machine Learning Engineer, Google Cloud Generative AI Leader, Microsoft Azure AI Engineer Associate, or equivalent AWS credentials.
- Preferred experience includes MCP servers, multi-agent patterns, LLM evaluation tooling such as LangSmith, Langfuse, or promptfoo, and Terraform or CI/CD pipelines.
Benefits
- Hybrid work model with flexibility for collaboration, client needs, and personal commitments.
- Medical, dental, and vision coverage, a 401(k) plan with company matching, and paid parental leave.
- Unlimited paid time off.
- Learning, development, knowledge sharing, and growth opportunities within a multidisciplinary AI, data, and consulting organization.
- Artefact sponsors the required AI or cloud certification exam and provides preparation time.
- Professional growth through client-facing work, mentoring, internal accelerators, and new challenges.
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