2 months 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.
Categories
About Artefact
Artefact is a Paris‑headquartered consulting and engineering firm that builds data and AI solutions for large enterprises, from strategy and governance to platforms, MLOps, and data‑driven marketing. It operates across 27 countries and works with brands such as L’Oréal and Sanofi. The company is privately held and monetizes through consulting engagements, industrialized AI projects, and managed data services.