Senior Deployed AI Engineer (Gemini) (PCS848)

💼 Senior Deployed AI Engineer (Gemini) 🌎 Peru, Argentina, Brazil 🏠 Remote About Our Client Our client is a global data and AI consulting firm serving enterprise clients across multiple industries, including FMCG, financial services, healthcare, manufacturing, and the public sector, with a team of data and AI experts spread across 20+ countries. About the Role We're looking for a Senior Deployed AI Engineer specialized in Gemini Enterprise and the Google AI stack to design, build, and deliver full-stack AI products for enterprise clients. You'll work embedded with clients, take AI features from idea to production, and serve as the team's reference for Google's enterprise AI platform. You'll 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. Beyond Google platform depth, you'll be expected to deliver confidently across the full stack, including full-stack development, data engineering, cloud infrastructure, and client communication. Responsibilities Develop user-facing interfaces in TypeScript and React, along with the backend services and APIs behind them in Python or Node Implement agentic behavior including orchestration, tool and function calling, memory, and guardrails Build retrieval-augmented generation pipelines covering ingestion, chunking, embeddings, and vector and hybrid search Design and build agents with Gemini models, Vertex AI, the Agent Development Kit, and Agent Engine Implement and configure Gemini Enterprise for clients, including Agent Designer, the Inbox for managing long-running agents, and agent sandboxes Connect Gemini Enterprise to client application landscapes through first-party and partner connectors with proper permissions, governance, and auditability Build grounded, retrieval-backed applications with Vertex AI Search, RAG Engine, grounding with Google Search, and BigQuery as the data backbone Implement agent interoperability through the A2A protocol and MCP, and track Google's releases closely to translate new capabilities into client value Write evaluation suites and regression tests for LLM-powered features, monitoring cost, latency, and quality in production Deploy on cloud infrastructure across GCP, Azure, or AWS, and build and maintain the data pipelines feeding AI systems Use agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor daily with good judgment about verification and review Communicate progress, trade-offs, and blockers clearly to clients and project leads, and support pre-sales when needed Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards What We're Looking For 3 to 5 years of software or data engineering experience, with extensive hands-on use of AI tools and LLM-based development over the past year Strong hands-on experience with the Google AI stack, including Gemini models,…