Senior Deployed AI Engineer (Gemini) (PCS848)
TypeFull-time job
LocationArgentina
Posted2 hours ago
💼 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.
Originally posted on Himalayas
🌎 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.
Originally posted on Himalayas
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