Senior Forward Deployed Engineer (Gemini Enterprise)
at Artefact
- Seniority
- Senior
- Location
- Montréal, Quebec, Canada
- Posted
- 5d ago
javascripttypescriptpythongoreactawsgcpazureterraformserverlesslangchainllm
at Artefact
<h3><strong>About Artefact</strong></h3> <p>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.</p> <p>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.</p> <p>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.</p> <h3><strong>The Role</strong></h3> <p>Artefact is looking for a Senior Deployed AI Engineer specialized in Gemini Enterprise and the Google AI stack: an engineer who works embedded with our clients and takes AI products from idea to production.</p> <p>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 front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works.</p> <p>This role combines deep, certified expertise in Google's enterprise AI stack (Gemini models, Vertex AI, and the Gemini Enterprise agent platform) with the ability to deliver end to end. Beyond your platform specialization, you will be expected to work confidently across the full delivery lifecycle — full-stack development, data engineering, cloud infrastructure, evaluation, and client communication.</p> <p>You will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you.</p> <h3><strong>What You'll Do</strong></h3> <p><strong>Build Full-Stack AI Applications, End to End</strong></p> <p>You will build AI products across the entire stack, from interface to infrastructure.</p> <ul> <li>Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node.</li> <li>Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails.</li> <li>Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search.</li> <li>Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP.</li> </ul> <h3><strong>Go Deep on Gemini Enterprise and the Google AI Stack</strong></h3> <p>You will be the team's reference for Google's enterprise AI platform.</p> <ul> <li>Design and build agents with Gemini models, Vertex AI, the Agent Development Kit (ADK), and Agent Engine.</li> <li>Implement and configure Gemini Enterprise for clients: Agent Designer for natural-language and trigger-based agents, the Inbox for managing long-running agents at scale, and agent sandboxes for autonomous problem-solving.</li> <li>Connect Gemini Enterprise to the client's application landscape through first-party and partner connectors, with proper permissions, governance, and auditability.</li> <li>Build grounded, retrieval-backed applications with Vertex AI Search and RAG Engine, grounding with Google Search, and BigQuery as the data backbone.</li> <li>Implement agent interoperability through the A2A protocol and MCP.</li> <li>Track Google's releases closely and translate new capabilities into client value quickly.</li> </ul> <h3><strong>Make AI Systems Production-Grade</strong></h3> <p>Our standard is production quality: systems that are reliable, monitored, and maintainable.</p> <ul> <li>Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.</li> <li>Apply solid engineering practice: version control, code review, automated testing, CI/CD, and observability.</li> <li>Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code.</li> <li>Build and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.</li> </ul> <h3><strong>Work AI-Natively and Client-Facing</strong></h3> <p>Our engineers work AI-natively and represent Artefact directly with clients.</p> <ul> <li>Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.</li> <li>Communicate progress, trade-offs, and blockers clearly to clients and project leads.</li> <li>Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.</li> <li>Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.</li> </ul> <h3><strong>What We're Looking For</strong></h3> <p><strong>Required Experience</strong></p> <ul> <li>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).</li> <li>Strong hands-on experience with the Google AI stack: Gemini models, Vertex AI, and ideally Gemini Enterprise or ADK — ideally with experience taking at least one solution to production on GCP.</li> <li>Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs.</li> <li>Experience with front-end development (React or similar) and at least one backend framework.</li> <li>Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framew