GCP Gemini AI Developer

Job Title: GCP Gemini AI Developer (3-5 Years Experience)
Location: 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 exists to operationalize advanced GenAI capabilities - including natural language understanding, multimodal reasoning, and generative automation - within scalable, secure, and production-ready cloud environments.
The developer will work 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
What Success Looks Like
Measurement 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 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. 3. RAG / Context-Aware Workflows Implement Retrieval-Augmented Generation (RAG) pipelines combining Gemini + Big Query or vector databases for knowledge grounding.

Demonstrated 25% reduction in hallucination or response variance. 4. Cross-Team Enablement Partner with Data, Automation, and App Dev teams to integrate Gemini AI into existing business workflows (e.g., UiPath, Power Platform, or Service Now). Minimum of 2 successful integrations with documented ROI. 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 Big Query
  , 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
Core Google Cloud Services

• Vertex AI, Generative AI Studio, Gemini API
• Big Query, Big Query ML, Dataform
• Cloud Run, Cloud Functions, Cloud Storage
• Pub/Sub, Secret Manager, IAM, Cloud Build

Programming Stack

• Python or Type Script (Google Cloud SDKs, google-generativeai, aiplatform)
• FastAPI / Flask / Node.js
• Lang Chain / Llama Index for orchestration
• SQL, Pandas, and Jupyter for data prep

Complementary Tools

• Terraform (IaC)
• Git Hub / Git Lab 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