Company

Technology

AIArchitect

$1500–2200k ~AI est. Prague, Czech Republic FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“AI Architect. Skills: AI Architecture, System Design, AI Governance. Define technical vision for AI. Establish reusable patterns”

Industry & Context.

Technology
Problems you'll solve

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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