Company
Technology
AIArchitect
Neural analysis suggests this role is
optimal for Senior candidates.
“AI Architect. Skills: AI Architecture, System Design, AI Governance. Define technical vision for AI. Establish reusable patterns”
Industry & Context.
Map unstructured business problems
What They're Looking For.
Must Have
8–12+ years of overall engineering experience, 3 years in AI/ML architecture, Portfolio of AI systems run in production for 1+ years
Nice to Have
Experience designing AI solutions for multiple teams
What You'll Do.
Define technical vision for AI
Establish reusable patterns
Establish reference architectures
Establish design principles
Design human-in-the-loop systems
Design multi-modal inputs
Package pattern libraries
Minimize technical debt
Map business problems to AI solutions
Run discovery workshops
Define AI governance standards
Define model versioning policies
Define prompt lifecycle management
Define evaluation cadence
Define deprecation strategies
Establish AI design principles
Establish architectural constraints
Design evaluation frameworks
Design golden datasets
Design regression benchmarks
Architect feedback loops
Translate business strategy into AI roadmap
How You'll Work.
Team & Collaboration
Enable teams to build AI systems
Communication Scope
Communicate complex architecture
Full Job Description
The AI Architect is responsible for defining the long-term technical vision for how AI is designed, structured, and scaled across the organization. Beyond building individual solutions, you will establish reusable patterns, reference architectures, and design principles that enable teams to build AI systems consistently, reliably, and at speed KEY RESPONSIBILITIES & SKILLS Architectural Design & Pattern Thinking - Design and deliver standard AI system blue prints such as agentic loops, RAG pipelines, human-in-the-loop systems, and multi-modal inputs which are packaged as internal platforms, SDKs, and pattern libraries that teams adopt and build on rather than reinvent from scratch. - Design for longevity, making architectural choices that age well and accumulate minimal technical debt over time - Map unstructured business problems to AI-native solutions not just fitting known tools to familiar patterns - Run discovery workshops or design sprints to surface the right AI approach before committing to a solution Governance & Standards - Define AI governance standards: model versioning policies, prompt lifecycle management, evaluation cadence, and deprecation strategies. Establish AI design principles across the organization guardrails, quality bars, and architectural constraints that apply to all AI products - Design long-lived evaluation frameworks golden datasets and regression benchmarks that track quality over months and years, not just at launch - Architect feedback loops that make AI systems self-improving over time through RLHF (Reinforcement Learning from Human Feedback), preference data collection, or active learning - Translate multi-year business strategy into a phased AI architecture roadmap Soft Skills & Experience - Systems thinker sees how decisions made today shape the architecture two years from now - Storyteller communicates complex architecture in a way that excites and aligns stakeholders, not just informs them - 8–12+ years of overall engineering e
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