GCP Gemini AI Developer

Job Title: GCP Gemini AI Developer (3–5 Years Experience)Location: Remote / Hybrid – Chicago preferredEmployment Type: Contract / Full-TimeReports To: GCP Technical Lead / AI Program ManagerPurposeThe 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 + BigQuery or vector databases for knowledge grounding.	Demonstrated 25% reduction in hallucination or response variance.
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.
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 EnvironmentCore Google Cloud Services

• 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