Product Engineering Architect
$31,200–$31,200 year
On-siteDraper, Utah, United States
Job Summary
Execute the Product Engineering AI roadmap by defining architecture standards, reusable frameworks, and scalable design patterns aligned with business objectives. Design and deliver enterprise-grade AI solutions, including Generative AI applications, Agentic AI systems, RAG pipelines, and LLM integrations, while conducting proofs of concept and evaluating emerging technologies. Partner with leadership, business stakeholders, and engineering teams to translate requirements into practical, innovative AI solutions that are scalable and governed. Collaborate with Security, Risk, Compliance, and Architecture teams to ensure responsible, secure, and compliant AI implementations, assessing risks and recommending mitigation strategies. Mentors engineers and architects by sharing best practices and fostering organizational AI fluency through reference architectures and reusable components.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Statistics, Data Science, or a related field
- Equivalent combination of education and experience
- Minimum 8 years of experience in solution architecture, technical design, and enterprise technology delivery
Desired Qualifications
- Master's degree
- Minimum 3 years of Strong knowledge of Generative AI, Large Language Models (LLMs), Agentic AI systems, and AI operational frameworks
- Experience leading technical initiatives and architecting complex solutions, particularly within regulated or highly governed environments
- Experience working with AI orchestration platforms and frameworks such as LangChain, CrewAI, AutoGen, Strands, or similar technologies
- Familiarity with cloud AI ecosystems including Azure AI Foundry, AWS Bedrock, GCP Gemini AI, M365 Copilot Studio, or comparable platforms
- Experience developing RAG solutions, vector databases, memory systems, prompt engineering strategies, and model optimization techniques
- Knowledge of modern AI development tools, coding assistants, and enterprise AI platforms
- Knowledge of integration protocols, tool-calling frameworks, and API-driven architectures
- Familiarity with responsible AI practices, governance principles, and regulatory frameworks
- Knowledge of LLM infrastructure concepts such as GPU utilization, token economics, and performance optimization
- Strong communication skills with an aptitude for translating technical concepts into business-focused recommendations
- Success collaborating with cross-functional teams in Agile and evolving technology environments
- Relevant AI, cloud, or architecture certifications
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