AI Summary
Design, develop, and deploy intelligent AI solutions on Google Cloud Platform. Leverage Vertex AI and Gemini models to build scalable AI systems. Collaborate with product, data, and platform teams to translate business needs into AI-driven solutions.
Key Highlights
AI Solution Architect
GenAI and Agentic AI Engineer
Google Cloud Platform
Vertex AI
Gemini models
Technical Skills Required
Benefits & Perks
Remote work
Full-time employment
Job Description
Position Name : AI Solution Architect
Location: 100% Remote
Position Type: Fulltime
We are seeking a highly skilled GenAI and Agentic AI Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and deploy intelligent AI solutions. The ideal candidate has hands-on experience with Vertex AI, Gemini models, and the development of agent-based systems that orchestrate tools, workflows, and reasoning at scale.
Key Responsibilities
- Design, develop, and deploy Generative AI solutions on Google Cloud Platform
- Build and productionize Agentic AI systems (multi-step reasoning, tool use, orchestration, memory)
- Leverage Vertex AI for:
- Model training, fine-tuning, evaluation, and deployment
- Pipelines, Feature Store, Model Registry, and monitoring
- Implement applications using Gemini models (Gemini 1.5 / latest) for:
- Text, multimodal, and reasoning-based use cases
- Function calling, tool integration, and structured outputs
- Architect scalable AI systems using GCP services (Cloud Run, GKE, BigQuery, Pub/Sub, Cloud Functions)
- Apply prompt engineering, RAG patterns, embeddings, and vector search
- Ensure security, compliance, cost optimization, and performance best practices on GCP
- Collaborate with product, data, and platform teams to translate business needs into AI-driven solutions
- Strong hands-on experience with Google Cloud Platform
- Proven experience in Generative AI and Agentic AI
- Direct experience with Vertex AI (training, pipelines, deployment, monitoring)
- Hands-on experience with Gemini models via Vertex AI or APIs
- Experience building AI agents (planning, reasoning, tool usage, orchestration)
- Proficiency in Python (primary) and familiarity with REST APIs
- Experience with LLM frameworks (LangChain, LlamaIndex, or similar)
- Experience with RAG architectures and vector databases (Vertex AI Vector Search, Pinecone, etc.)
- Knowledge of MLOps and CI/CD for AI workloads
- Experience deploying AI workloads on Cloud Run or GKE
- Familiarity with multimodal AI (text, image, document understanding)
- GCP certifications (Professional ML Engineer, Cloud Architect)
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