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# Generative AI (GenAI) Architect with Google Cloud Platform

- **Company:** Tekshapers Inc
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Cloud Computing Security, Distributed Systems, Python (Programming Language), Google Cloud, Feature Engineering, Data Ingestion, Large Language Models, Model Validation, Generative AI, Api Design, Microservices
- **Published:** September 15, 2026
- **Apply:** https://www.dice.com/job-detail/26d0cf29-0036-4191-a7a0-294a140c6b6e

## About the Role

We are seeking a highly skilled Senior Generative AI Architect to lead the design, development, and deployment of enterprise-grade GenAI solutions. The ideal candidate will possess deep technical expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and cloud-native architecture. This role requires hands-on proficiency, strong architectural thinking, and the ability to translate complex business problems into scalable AI-driven systems., 1. 12+ years of experience in AI/ML, with at least 3+ years focused on Generative AI architectures.

 2. Demonstrated hands-on projects with LLMs, fine-tuning, RAG implementation, or agentic workflows.
 3. Portfolio of real-world deployments, POCs, or production systems.

Technical Expertise

 * Strong foundation in LLM architecture, transformer models, model fine-tuning, and model evaluation techniques.
 * Deep, hands-on experience designing RAG, agentic, and LLM-orchestration pipelines.
 * Proficiency in Python and GenAI frameworks; ability to write, review, and optimize production-grade code.
 * Solid understanding of computer vision, NLP, and multimodal systems is a plus-but must be secondary to GenAI expertise.

Architecture & Cloud

 * Proven ability to design scalable, secure cloud architectures using AWS/Azure/Google Cloud Platform.
 * Experience with API design, microservices, event-driven workflows, and distributed systems.
 * Clear understanding of data ingestion, feature engineering, vector databases, and embeddings lifecycle.



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