Job description

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 + 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 Environment Core 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

Requirements

Must-haves

Job Title: GCP Gemini AI Developer (3–5 Years Experience)
Employment Type: Contract / Full-Time
Reports To: GCP Technical Lead / AI Program Manager
Key Performance Outcomes (6–12 Months)
What Success Looks Like
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
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.
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.
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.
Continuous Optimization
Monitor, retrain, and improve AI models via Vertex AI pipelines and Model Monitoring .
Demonstrated 15% performance gain over baseline models.
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 .

Nice-to-haves

Location: Remote / Hybrid – Chicago preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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