Senior Deployed AI Engineer (Gemini) (PCS933)

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4d•Remote

About The Position

A global data and AI consulting firm is seeking a Senior Deployed AI Engineer specializing in Gemini Enterprise and the Google AI stack. The role designs, builds, and delivers full-stack AI products for enterprise clients, working directly with clients to take AI features from concept through production. The engineer owns components end to end, including the user interface, services, data and retrieval pipelines, deployment, and evaluation. The role also requires full-stack development, data engineering, cloud infrastructure, and effective client communication. The position is fully remote and full-time, with dedicated hours for team alignment and availability during US business hours. Compensation is paid in USD twice monthly, on the 15th and 30th. Paid time off and holidays follow company policy and guidelines. A Google Cloud certification is a strong differentiator; candidates without one must obtain one within their first two months, with exam sponsorship and preparation time provided. The preferred certification is Google Cloud Professional Machine Learning Engineer. Applicants must submit an updated resume, a GitHub link or portfolio showing production AI or full-stack work, and a 1–2 minute Loom video introducing themselves and describing an end-to-end production AI system they owned on the Google stack, including their approach to evaluation and reliability. Both a portfolio and Loom video are required to advance.

Requirements

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, and ideally Gemini Enterprise or ADK; 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 experience 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.
Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.
Candidates without a Google Cloud certification must obtain one within the first two months; sponsorship and preparation time are provided.

Nice To Haves

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.
A Google Cloud certification; the preferred certification is Google Cloud Professional Machine Learning Engineer.

Responsibilities

Develop user-facing interfaces in TypeScript and React, as well as 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 using first-party and partner connectors, with appropriate permissions, governance, and auditability.
Build grounded, retrieval-backed applications using 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 to translate new capabilities into client value.
Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.
Deploy on cloud infrastructure across GCP, Azure, or AWS, and build and maintain data pipelines feeding AI systems.
Use agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor daily, applying sound judgment to 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.