2 months ago
Montréal, CanadaMid Level / Senior
Responsibilities
- Build full-stack AI applications with TypeScript/React interfaces and Python or Node backend services and APIs.
- Develop agentic systems using orchestration, tool and function calling, memory, and guardrails.
- Build retrieval-augmented generation pipelines involving ingestion, chunking, embeddings, vector search, and hybrid search.
- Design and deploy agents using Gemini models, Vertex AI, the Agent Development Kit, Agent Engine, and Gemini Enterprise.
- Configure Gemini Enterprise features, connectors, permissions, governance, auditability, and agent interoperability through A2A and MCP.
- Create evaluation suites and regression tests for LLM-powered features and monitor production cost, latency, quality, and reliability.
- Deploy and maintain AI systems on GCP, Azure, or AWS using containers, serverless infrastructure, and infrastructure-as-code.
- Build and maintain data pipelines across warehouses, lakehouses, and vector stores.
- Communicate progress and technical trade-offs to clients, support pre-sales demos and solution scoping, and mentor junior engineers.
- Contribute to internal accelerators, reusable components, engineering standards, code reviews, automated testing, CI/CD, and observability.
Requirements
- 3–5 years of software engineering or data engineering experience, including extensive hands-on AI tools and LLM-based development during the past year.
- Strong hands-on experience with Gemini models and Vertex AI, ideally including Gemini Enterprise or the Agent Development Kit and production delivery on GCP.
- Strong Python and TypeScript/JavaScript programming skills and experience building and consuming APIs.
- Experience with front-end development such as React and at least one backend framework.
- Hands-on experience with RAG, embeddings, vector search, and an agentic framework such as Google ADK, LangGraph, or LangChain.
- Strong GCP experience; Azure or AWS experience is a plus.
- Experience using 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.
- Google Cloud certification is preferred; candidates without one must obtain it within their first two months.
- Preferred experience includes MCP servers, multi-agent patterns, LLM evaluation tools such as LangSmith, Langfuse, or promptfoo, Terraform or CI/CD pipelines, and GCP, BigQuery, or Google Workspace integrations.
Benefits
- Artefact sponsors the Google Cloud certification exam and provides preparation time when certification is not already held.
- Work directly with clients on complex, high-impact enterprise AI projects and join a global community of data and AI experts.
- Receive opportunities for learning, mentorship, knowledge sharing, and contribution to reusable engineering accelerators.
Tech Stack
Categories
Forward Deployed
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.