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Artefact

Data and AI-driven business transformation consulting

Senior Deployed AI Engineer - Gemini Enterprise

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeSão Paulo, State of São Paulo, BrazilNo salary listedRemote

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.
  • Strong hands-on experience with the Google AI stack, including Gemini models and Vertex AI; experience with Gemini Enterprise or the Agent Development Kit is preferred, as is having taken at least one solution to production on Google Cloud Platform.
  • 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, vector search, and at least one agentic framework such as Claude Agent SDK or LangGraph/LangChain.
  • Strong working experience with Google Cloud Platform; Azure or Amazon Web Services experience is a plus.
  • Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.
  • Experience building and maintaining data pipelines.
  • Professional English proficiency at C1/C2 minimum is mandatory.
  • A bachelor's or master's degree in computer science, engineering, or a related field, or equivalent practical experience.
  • If not already held, a Google Cloud certification must be obtained within the first two months in the role; the employer 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.js.
  • 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.
  • Design and build agents using Gemini models, Vertex AI, the Agent Development Kit, and Agent Engine.
  • Implement and configure Gemini Enterprise for clients, including 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.
  • Connect Gemini Enterprise to client applications through first-party and partner connectors with appropriate permissions, governance, and auditability.
  • Build grounded, retrieval-backed applications with Vertex AI Search and RAG Engine, grounding with Google Search, and BigQuery as the data backbone.
  • Implement agent interoperability through the A2A protocol and MCP.
  • Track Google's releases and translate new capabilities into client value.
  • Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.
  • Apply version control, code review, automated testing, continuous integration and continuous delivery, and observability practices.
  • Deploy using cloud infrastructure on Google Cloud Platform, Azure, or Amazon Web Services, including containers, serverless technologies, 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, verifying and reviewing their output.
  • 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 tools such as LangSmith, Langfuse, or promptfoo.
  • Experience with Terraform or continuous integration and continuous delivery pipelines.
  • Experience with Google Cloud Platform, BigQuery, or Google Workspace integrations alongside Gemini Enterprise.
  • A Google Cloud Professional Machine Learning Engineer certification is preferred.
  • A Google Cloud Generative AI Leader certification is valued.

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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