RemoteSeniorFull TimeArtificial Intelligence
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Data EngineeringMachine LearningAWSGoogle Cloud PlatformJavaScriptMicrosoft AzureNode.jsPythonGitHubReactTerraformTypeScriptTime Off: Paid (PTO)Stipend: Home Office
💼 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, Vertex AI, and ideally Gemini Enterprise or ADK, with at least one solution taken to production on GCP
- Strong programming skills in Python and TypeScript or JavaScript, with experience building and consuming APIs
- Experience with front-end development in React or similar frameworks, and at least one backend framework
- Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework such as Google ADK, LangGraph, or LangChain
- Strong working experience with GCP; Azure or AWS is a plus
- Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor, and experience building and maintaining data pipelines
- Professional English proficiency at C1 or C2 level minimum, as you'll work daily with international clients and colleagues
- Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience
- A Google Cloud certification is a strong differentiator at application; if you don't hold one yet, obtaining one within the first two months is required, with exam sponsorship and prep time provided. The preferred certification is Google Cloud Professional Machine Learning Engineer, covering Vertex AI, generative AI, and production ML
Nice to Have
- Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling such as LangSmith, Langfuse, or promptfoo
- Experience with Terraform or CI/CD pipelines
- Experience with GCP, BigQuery, or Google Workspace integrations alongside Gemini Enterprise
Work Schedule
- 100% remote setup so you can work wherever you're most productive
- This position operates on a full-time basis, with dedicated hours to ensure alignment with the team and delivery of quality work
- Availability during US business hours
Compensation & Time Off
- Compensation paid in USD
- Paid bi-monthly on the 15th and 30th
- Paid Time Off according to company policy
- Holidays observed according to company guidelines
Application Requirements
Please submit:
- An updated resume
- A GitHub link or portfolio showing AI systems or full-stack projects you've built and shipped in production, with a focus on the Google AI stack or agentic work
- A 1–2 minute Loom video introducing yourself, walking through one production AI system you owned end to end on the Google stack, and explaining how you approached evaluation and reliability alongside the feature build
Only candidates who submit both a portfolio and Loom video will be moved to the next step of the hiring process.
