AI Architect

KēSTA I.T.•Salt Lake, UT

3d•Hybrid

About The Position

We’re seeking an experienced Senior AI Solutions Architect to help shape and deliver scalable, enterprise-grade artificial intelligence solutions. This individual will provide technical leadership across AI architecture and engineering initiatives, establishing reusable design patterns, modern frameworks, and architectural standards that support business objectives. The ideal candidate brings extensive experience in enterprise solution architecture, Generative AI, Large Language Models (LLMs), and emerging Agentic AI technologies. This role will collaborate with business and technology leaders to transform complex requirements into secure, practical AI solutions while advancing responsible AI practices, technical innovation, and operational excellence.

Requirements

Bachelor’s degree in Computer Science, Engineering, Statistics, Data Science, or a related discipline, or an equivalent combination of education and experience.
8+ years of experience in solution architecture, technical design, and enterprise technology delivery.
Experience leading complex technical initiatives and designing enterprise solutions, particularly in regulated or highly governed environments.
Strong understanding of Generative AI, Large Language Models (LLMs), Agentic AI, and the operational frameworks used to deploy and manage AI solutions.
Hands-on experience with AI orchestration frameworks and agent development technologies, such as LangChain, CrewAI, AutoGen, Strands, or comparable solutions.
Familiarity with enterprise cloud AI platforms and ecosystems, including Azure AI Foundry, AWS Bedrock, Google Gemini AI, Microsoft 365 Copilot Studio, or similar technologies.
Experience designing and implementing RAG architectures, vector databases, AI memory systems, prompt engineering approaches, and model optimization techniques.
Knowledge of modern AI engineering tools, enterprise AI platforms, and AI-assisted software development technologies.
Understanding of API-driven architectures, integration protocols, tool-calling mechanisms, and frameworks supporting AI system interoperability.
Familiarity with responsible AI principles, governance practices, and applicable regulatory considerations.
Understanding of LLM infrastructure and performance considerations, including GPU utilization, token economics, and optimization strategies.
Strong communication and stakeholder engagement skills, with the ability to explain complex technical concepts and present business-focused recommendations.
Demonstrated ability to collaborate across multidisciplinary teams in Agile and rapidly evolving technology environments.

Nice To Haves

A Master’s degree is preferred.
3+ years of relevant experience preferred.
Master’s degree in Computer Science, Engineering, Data Science, or a related technical discipline.
Relevant certifications in artificial intelligence, cloud technologies, or enterprise and solution architecture.
Experience establishing enterprise AI architectural standards, reusable frameworks, and governance practices across multiple initiatives.

Responsibilities

Lead the architectural direction and implementation of enterprise AI initiatives, establishing scalable design standards, reusable frameworks, and technical patterns that support evolving business needs.
Architect, develop, and deliver sophisticated AI-powered solutions, including Generative AI applications, autonomous agent systems, LLM integrations, Retrieval-Augmented Generation (RAG), and AI operations frameworks.
Work closely with business partners, engineering teams, and technology stakeholders to translate strategic objectives into actionable technical solutions.
Research emerging AI capabilities, assess new technologies, lead proofs of concept, and develop recommendations based on business value, scalability, and technical feasibility.
Establish reusable architectural assets, reference designs, and shared components that improve consistency, accelerate delivery, and strengthen operational performance.
Partner with security, risk, compliance, and enterprise architecture teams to ensure AI solutions align with organizational governance, security, and responsible technology practices.
Identify architectural and implementation risks, develop mitigation strategies, and contribute to governance standards supporting enterprise-wide AI adoption.
Provide technical mentorship to engineers and architects, promoting knowledge sharing, engineering excellence, innovation, and continuous improvement.