4 days 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
