Senior Deployed AI Engineer, Gemini

Hire Overseas

. Design, build, and deliver full-stack AI products for enterprise clients .

Posted 11/3/2026full-timeRemote • Argentina, Peru, BrazilSenior💰 $45,000 - $65,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in full-stack AI product development, with strong capabilities in building user-facing interfaces and backend services using TypeScript, React, and Python. Proficient in deploying solutions on GCP and utilizing the Google AI stack, including Gemini models and Vertex AI.

Highest-signal resume keywords

Full-Stack AI Product DevelopmentGoogle AI Stack ExpertiseTypeScript and React DevelopmentPython Backend DevelopmentData Pipeline Maintenance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

TypeScriptReactPythonAPI DevelopmentRAGEmbeddingsVector SearchAgentic FrameworksData Pipeline MaintenanceGCP

Soft Skills

CommunicationMentoringProblem-Solving

Tools & Technologies

Gemini ModelsVertex AIGoogle CloudAzureAWSClaude CodeGemini CLICodexCursorAgent Development Kit

Certifications & Qualifications

Google Cloud Certification

Industry Keywords

AI ProductsEnterprise ClientsAgentic BehaviorOrchestrationTool and Function CallingMemoryGuardrailsEvaluation SuitesRegression TestsProduction Monitoring

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudGoogle Cloud PlatformJavaScriptNode.jsPythonReactTypeScript

About the role

Key responsibilities & impact

Design, build, and deliver full-stack AI products for enterprise clients
Develop user-facing interfaces in TypeScript and React
Build backend services and APIs 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, including Agent Designer, Inbox, and agent sandboxes
Connect Gemini Enterprise to client application landscapes through first-party and partner connectors with permissions, governance, and auditability
Build grounded retrieval-backed applications with Vertex AI Search, RAG Engine, Google Search grounding, and BigQuery
Implement agent interoperability through A2A and MCP
Track Google's releases and translate new capabilities into client value
Write evaluation suites and regression tests for LLM-powered features
Monitor production cost, latency, and quality
Deploy on GCP, Azure, or AWS
Build and maintain data pipelines feeding AI systems
Use agentic coding tools daily with verification and review
Communicate progress, trade-offs, and blockers to clients and project leads
Support pre-sales when needed
Mentor junior engineers
Contribute to internal accelerators, reusable components, and engineering standards

Requirements

What you’ll need

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 and Vertex AI; ideally Gemini Enterprise or ADK
At least one solution taken to production on GCP
Strong programming skills in Python and TypeScript or JavaScript
Experience building and consuming APIs
Front-end development experience in React or similar frameworks
Experience with at least one backend framework
Hands-on experience with RAG, embeddings, and vector search
Experience 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 Claude Code, Gemini CLI, Codex, or Cursor
Experience building and maintaining data pipelines
Professional English proficiency at C1 or C2 level minimum
Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience
Google Cloud certification is required within the first two months if not already held
Updated resume
GitHub link or portfolio showing AI systems or full-stack projects built and shipped in production
1–2 minute Loom video introducing yourself, presenting one production AI system owned end to end on the Google stack, and explaining evaluation and reliability approach
Must submit both a portfolio and Loom video to proceed to the next hiring step
Availability during US business hours

Benefits

Comp & perks

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
100% remote setup
Exam sponsorship and prep time provided for obtaining a Google Cloud certification
Full-time schedule with dedicated hours for team alignment and quality delivery