Google Gemini AI Architect
AceStack LLC
Google Gemini Ai Architect
Location - Cleveland, OH FTE
Job Description
Must Have Technical/Functional Skills
• 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, BigQuery, and Google Kubernetes Engine (GKE), Vector databases
• Cloud proficiency: Requires strong experience with Google Cloud Platform (GCP) and infrastructure-as-code (Terraform).
• Certifications: Google Cloud Professional Certifications, such as Professional Cloud Architect or Machine Learning Engineer.
• Demonstratable experience on GCP
• Verbal and written communication skills
Roles & Responsibilities
We are looking for an Google Gemini AI Architect with strong manufacturing domain expertise and consulting mindset to help industrial enterprises design, deploy, and scale data and AI platforms across plants and enterprise systems
Travel: 25–30% (plants, client workshops, exec reviews)
Responsibilities
• 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.
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