Google Gemini AI Architect

Centraprise Corp

Cleveland, OH, United States

about 2 months ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Starter

Experience required

1 year minimum

Working hours

Regular working hours

Job source

dice.com

Tech stack

Artificial Intelligence BigQuery Cloud Computing Python (Programming Language) Machine Learning Performance Tuning Systems Integration Google Cloud Enterprise Software Applications Large Language Models Multi-Agent Systems Prompt Engineering

+6 more

IT Architecture Generative AI Kubernetes Terraform Automation Anywhere Data Generation

Job description

AI architecture and design: Architecting end-to-end Generative AI solutions, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent systems, and prompt engineering strategies.
Vertex AI and Gemini integration: Integrating Gemini Pro/Ultra models into enterprise applications, managing fine-tuning, evaluation, and inference optimization.
Security and governance: Implementing AI guardrails, ensuring security, data privacy, and compliance within AI workflows (e.g., managing data residency and access controls).
Strategic advisory: Acting as a technical advisor to C-level stakeholders, defining roadmaps, ROI, and best practices for adopting Google Gemini Enterprise.
Prototyping and development: Leading hands-on development of Proofs of Concept (PoCs) and Minimum Viable Products (MVPs) to validate designs.

Innovation and Experimentation

Evaluate and prototype new tools and techniques in autonomous agents, synthetic data generation, multi-modal models, and AI orchestration.
Collaborate with product and business teams to conceptualize AI-powered assistants, copilots, and automation flows.

Leadership and Mentorship

Provide technical leadership to data scientists, prompt engineers, and AI developers.
Promote a culture of innovation, experimentation, and measurable business impact through AI.

Requirements

Experience: Requires 6+ years in solutions architecture, AI/ML engineering, or technical consulting. The experience should include at least 1-2+ years focused specifically on LLMs and Generative AI in production.
Technical skills: Requires deep knowledge of Vertex AI, Python, Big Query, and Google Kubernetes Engine (GKE), Vector databases
Cloud proficiency: Requires strong experience with Google Cloud Platform (Google Cloud Platform) and infrastructure-as-code (Terraform).
Certifications: Google Cloud Professional Certifications, such as Professional Cloud Architect or Machine Learning Engineer.
Demonstratable experience on Google Cloud Platform
Verbal and written communication skills, * Coordination and collaboration with multiple stakeholders
Guiding and leading the team members
Strong communication skills