AI & Agentic Engineer, Senior

Artefact•Montreal, QC

1d•Hybrid

About The Position

Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain. We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise — we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions. As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption. Artefact is looking for a Senior AI & Agentic Engineer: a full-stack engineer who takes AI features from idea to production. You will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the React front end, the Python or Node service behind it, the RAG pipeline feeding it, and the evaluations proving it works. This role combines breadth and depth: the ability to take a feature from front end to cloud deployment, together with strong expertise in at least one major AI platform — Google (Gemini), Anthropic (Claude), or OpenAI. You will work with direct client exposure, and you will support the professional development of the junior engineers around you.

Requirements

3–5 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 (professional projects, internal initiatives, or substantial personal builds).
Professional English proficiency mandatory. You will work daily with international clients and colleagues.
Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs.
Experience with front-end development (React or similar) and at least one backend framework.
Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (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.
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 a strong differentiator at application. If you do not hold one yet, obtaining one within your first 2 months in the role is a requirement — Artefact sponsors the exam and gives you time to prepare.

Nice To Haves

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

Responsibilities

Build AI products across the entire stack, from interface to infrastructure.
Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node.
Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails.
Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search.
Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP.
Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.
Apply solid engineering practice: 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 the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.
Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.
Communicate progress, trade-offs, and blockers clearly to clients and project leads.
Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.
Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.