Gemini Architect

Envision Technology Solutions; Rahway (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

Envision Technology Solutions seeks a Senior AI/Cloud Architect to design scalable AI architectures on Google Cloud, integrate LLMs with enterprise data, and optimize performance for low latency. You will guide teams and ensure governance for AI workloads.

Strong background in cloud engineering, software architecture, and machine learning is required, with experience in prompt engineering and RAG. Familiarity with UiPath and automation platforms is a plus.

Qualifications

• 5+ years in software architecture, cloud engineering, or machine learning.

Responsibilities

• Design scalable architectures using Google Cloud and Gemini models.
• Integrate LLMs with enterprise data sources, APIs, and third-party connectors.
• Tune model outputs, manage token limits, and reduce latency.
• Lead technical and business teams on AI capabilities and deployment.
• Ensure data privacy, compliance, and governance for AI workloads.

Skills

Senior architecture

GCP

Vertex AI

Python

REST APIs

Vector databases

Prompt engineering

RAG

Agentic systems

UiPath

Tools

UiPath

Job description

• Experience: 5+ years in software architecture, cloud engineering, or machine learning.
• Cloud Skills: Strong expertise in Google Cloud Platform (GCP) and Vertex AI.
• Coding: Proficiency in Python, REST APIs, and vector databases.
• AI Knowledge: Deep understanding of prompt engineering, RAG (Retrieval-Augmented Generation), and agentic systems.
• Automation: Experience in Automation platforms like UiPath (preferred) or equivalent

Key Responsibilities

• Design Systems: Understand Business problems and build scalable architectures using Google Cloud and Gemini models. Ability to understand current landscape of Automations and propose solutions to upgrade to Agents through scalable architecture
• Integrate AI: Ability to connect LLMs with enterprise data sources, APIs, and third-party connectors.
• Optimize Performance: Should be able to tune model outputs, manage token limits, and reduce latency.
• Lead Strategy: Guide technical and business teams on AI capabilities and deployment.
• Ensure Security: Implement data privacy, compliance, and governance standards for AI workloads.