Senior Deployed AI Engineer – Gemini

Hire Overseas

. Develop user-facing interfaces in TypeScript and React, with backend services and APIs in Python or Node .

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

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing user-facing interfaces using TypeScript and React, alongside backend services in Python or Node. Proficient in implementing AI tools, particularly within the Google AI stack, and building data pipelines for AI systems.

Highest-signal resume keywords

TypeScript DevelopmentReact Front-End DevelopmentPython Backend DevelopmentGoogle AI Stack ExpertiseData Pipeline Construction

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

TypeScriptReactPythonAPIsRAGEmbeddingsVector SearchGemini ModelsVertex AIData Pipelines

Soft Skills

CommunicationMentoringProblem-Solving

Tools & Technologies

GCPAzureAWSClaude CodeGemini CLICodexCursorBigQueryAgent Development KitMCP

Certifications & Qualifications

Google Cloud CertificationGoogle Cloud Professional Machine Learning Engineer

Industry Keywords

AI ToolsLLM DevelopmentAgentic FrameworksMulti-Agent PatternsCI/CD Pipelines

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudGoogle Cloud PlatformJavaScriptNode.jsPythonReactTerraformTypeScript

About the role

Key responsibilities & impact

Develop user-facing interfaces in TypeScript and React, with 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 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 permissions, governance, and auditability
Build grounded, retrieval-backed applications with Vertex AI Search, RAG Engine, grounding with Google Search, and BigQuery
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
Monitor cost, latency, and quality in production
Deploy on GCP, Azure, or AWS
Build and maintain data pipelines feeding AI systems
Use Claude Code, Gemini CLI, Codex, or Cursor 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
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
At least one solution taken to production on GCP
Strong programming skills in Python and TypeScript or JavaScript
Experience building and consuming APIs
Experience with front-end development 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
Preferred certification: Google Cloud Professional Machine Learning Engineer
Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling is nice to have
Experience with Terraform or CI/CD pipelines is nice to have
Experience with GCP, BigQuery, or Google Workspace integrations alongside Gemini Enterprise is nice to have
Availability during US business hours
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 and walking through one production AI system owned end to end
Portfolio and Loom video are both required to advance to the next hiring step

Benefits

Comp & perks

Paid Time Off according to company policy
Holidays observed according to company guidelines
Compensation paid in USD
Paid bi-monthly on the 15th and 30th
Exam sponsorship and prep time provided for obtaining a Google Cloud certification
100% remote setup
Dedicated full-time hours for team alignment and quality delivery