GCP Gemini AI Developer

Job Title GCP Gemini AI Developer (3–5 Years Experience)

Location & Employment Remote / Hybrid – Chicago preferred Employment Type:

Contract / Full-Time Reports To:

GCP Technical Lead / AI Program Manager

Purpose The

GCP Gemini AI Developer

will design, build, and deploy intelligent applications leveraging

Google Cloud’s Gemini models and Vertex AI platform . This role operationalizes advanced GenAI capabilities – including natural language understanding, multimodal reasoning, and generative automation – within scalable, secure, and production‑ready cloud environments. The developer works hands‑on across data engineering, AI model orchestration, and API integration to create

AI‑driven business solutions

that reduce manual effort, enhance decision‑making, and unlock measurable value from enterprise data.

Key Performance Outcomes (6–12 Months)

Outcome 1: Gemini‑Powered Solutions Deployed

– Design, develop, and deploy at least two Gemini‑based AI solutions (e.g., document summarization, chat agent, or data extraction automation) using Vertex AI + Gemini APIs. Delivered to production with >90% accuracy and

Outcome 2: Scalable Cloud Architecture

– Build a modular AI microservices framework using Cloud Run / Cloud Functions with integrated authentication, logging, and monitoring. Reusable components adopted in at least 3 future use cases.

Outcome 3: RAG / Context‑Aware Workflows

– Implement Retrieval‑Augmented Generation (RAG) pipelines combining Gemini + BigQuery or vector databases for knowledge grounding. Demonstrated 25% reduction in hallucination or response variance.

Outcome 4: Cross‑Team Enablement

– Partner with Data, Automation, and AppDev teams to integrate Gemini AI into existing business workflows (e.g., UiPath, Power Platform, or ServiceNow). Minimum of 2 successful integrations with documented ROI.

Outcome 5: Continuous Optimization

– Monitor, retrain, and improve AI models via Vertex AI pipelines and Model Monitoring. Demonstrated 15% performance gain over baseline models.

Core Responsibilities

Design and deploy

Gemini 1.5 Pro/Flash

integrations via

Vertex AI and Generative AI Studio .

Build

serverless APIs

and backend services for AI workflows using

Cloud Run ,

Functions , or

App Engine .

Develop

data ingestion and preprocessing pipelines

using

BigQuery ,

Dataform , and

Pub/Sub .

Apply

prompt engineering

and

parameter tuning

to improve generative model accuracy.

Implement

RAG pipelines

leveraging

Vertex Matching Engine

or

Pinecone .

Collaborate with automation and data teams to embed AI into existing business processes.

Maintain compliance with security, privacy, and model governance standards.

Technical Environment

Vertex AI, Generative AI Studio, Gemini API

BigQuery, BigQuery ML, Dataform

Cloud Run, Cloud Functions, Cloud Storage

Pub/Sub, Secret Manager, IAM, Cloud Build

Programming Stack

Python or TypeScript (Google Cloud SDKs, google-generativeai, aiplatform)

FastAPI / Flask / Node.js

LangChain / LlamaIndex for orchestration

SQL, Pandas, and Jupyter for data prep

Complementary Tools

Terraform (IaC)

GitHub / GitLab CI/CD

Vertex AI Pipelines & Model Registry

Vector DB (Vertex Matching Engine, Pinecone, or Weaviate)

Ideal Profile

3–5 years hands‑on GCP development experience with AI/ML exposure

Strong working knowledge of

Vertex AI ,

Gemini models , and

RAG pipeline design

Demonstrated ability to move AI prototypes into production

Strong communicator, able to collaborate across automation, data, and cloud teams

Curious problem‑solver passionate about applied AI innovation

Success Metrics

Speed to Delivery:

End‑to‑end deployment within 8–10 weeks per use case

Model Effectiveness:

>90% accuracy or relevance rating from business stakeholders

Scalability:

Framework reused for ≥3 additional AI initiatives

Business Impact:

25%+ improvement in productivity or efficiency from deployed use cases

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