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Artefact

Data and AI-driven business transformation consulting

AI & Agentic Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeMontreal, QC, CanadaNo salary listed

About the Job

Requirements

  • Three to five years of experience in software engineering or data engineering, with extensive hands-on use of AI tools and LLM-based development over the past year through professional projects, internal initiatives, or substantial personal builds.
  • Professional English proficiency is mandatory.
  • Strong programming skills in Python and TypeScript or JavaScript, and experience building and consuming APIs.
  • Experience with front-end development using React or a similar framework, and at least one backend framework.
  • Hands-on experience with retrieval-augmented generation, embeddings, and 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: Google (Gemini, Vertex AI, Gemini Enterprise), Anthropic (Claude, Managed Agents, MCP), or OpenAI (Responses API, AgentKit); and working experience with one cloud platform (GCP, Azure, or AWS).
  • Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.
  • Experience building and maintaining data pipelines.
  • A Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.
  • A certification on at least one major AI platform or cloud is required within the first two months in the role if not already held; Artefact sponsors the exam and provides preparation time.

Responsibilities

  • Develop user-facing interfaces in TypeScript and React, and backend services and APIs in Python or Node.
  • Implement agentic behavior, including orchestration, tool and function calling, memory, and guardrails.
  • Build retrieval-augmented generation pipelines covering ingestion, chunking, embeddings, vector search, and hybrid search.
  • Connect AI systems to enterprise data and applications through APIs, semantic layers, and protocols such as MCP.
  • Write evaluation suites and regression tests for LLM-powered features, and monitor production cost, latency, and quality.
  • Apply engineering practices including version control, code review, automated testing, CI/CD, and observability.
  • Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure as code.
  • Build and maintain data pipelines that feed AI systems across warehouses, lakehouses, and vector stores.
  • Use agentic coding tools such as Claude Code, Gemini CLI, Codex, and Cursor daily, applying judgment in verification and review.
  • Communicate progress, trade-offs, and blockers to clients and project leads.
  • Support pre-sales by scoping solutions, building demos, and estimating effort with partnership and consulting teams.
  • Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.

Desired Qualifications

  • Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling such as LangSmith, Langfuse, or promptfoo.
  • Experience with Terraform or CI/CD pipelines.

About the Company

Artefact

Data and AI-driven business transformation consulting

Artefact helps organizations use data and AI to transform their operations and decision-making. It provides end-to-end services across the data value chain, from strategy and data platform implementation to governance, advanced analytics, and sector-specific AI tools, delivered through consulting and project execution. The company works with industries like FMCG, retail, and luxury goods to optimize marketing performance, customer engagement, and ROI. Artefact differentiates itself by combining consulting expertise with data science and AI capabilities across multiple regions, offering integrated, sector-focused solutions rather than just advisory services. The main goal is to help clients adopt data-driven approaches to improve efficiency, decision quality, and marketing results at scale.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Paris, France
Founded
1998

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