One of the fastest-growing software development companies in the Hungarian IT sector, founded in 2010 and currently employing 70 people, specializes in modern web and mobile technologies, providing custom software development services to our clients. No startup hype. No enterprise slow-down.

What makes us different:

• Dedicated research unit – building reusable components and real project impact, not slides
• Code quality is non-negotiable – supported by deliberate processes and engineering culture
• Full lifecycle ownership – from vision to production, working closely with BA, UX, PM, Dev, QA, and DevOps
• Real value delivery – agile, client-focused, outcome-driven

We are looking for a Solution Architect with strong AI readiness, someone who not only designs complex systems but also understands when and how to leverage AI in a real business context.

This is not a traditional architect role where “AI knowledge is a nice-to-have.” We are looking for someone who has already made the shift: AI is not an optional add-on, but a core architectural building block.

You design systems with this in mind, make decisions accordingly, and communicate it clearly, including when AI is not the right solution.

If over the past year you haven’t just explored AI, but have actually integrated it into real systems (LLMs, RAG pipelines, agent-based solutions), we would like to hear from you.

What we will trust you with:

• Aligning business and technology: translating client goals into feasible, sustainable technical solutions, evaluating trade-offs, and anticipating 6–24 month architectural impacts. Proactively identifying AI opportunities and risks, and using AI tools to improve decision-making and efficiency.
• Designing system and AI architectures: defining boundaries, components, integrations, interfaces, and data flows; creating high-level and detailed plans, diagrams, and AI-specific architectures (inference pipelines, model serving, embeddings, guardrails, feedback loops, cost models, prompt strategies).
• Owning architectural decisions: making, documenting, and justifying technical choices; balancing cost, risk, and business value; escalating high-impact or compliance-sensitive decisions. For AI: build vs. buy vs. integrate, model/service selection, vendor lock-in evaluation.
• Ensuring quality and NFRs: defining and enforcing performance, scalability, security, availability, maintainability, and observability. For AI: accuracy, latency, cost per inference, hallucination, drift, safety, auditability, model monitoring, and guardrails.
• Supporting delivery: participating in kick-offs, design sessions, backlog refinements; validating architecture through reviews, ensuring alignment, and guiding implementation. For AI: monitoring model performance, validating feedback loops, and supporting evaluation methodology with BA and QA.
• Managing risks and technical debt: identifying blockers, mitigating risks, tracking and communicating technical debt, maintaining tech roadmap, escalating project-critical issues. For AI: bias, drift, hallucination, PII, compliance (GDPR/AI Act), cost, and technology evolution.
• Stakeholder communication and leadership: translating technical and business language, facilitating discussions, clearly communicating AI capabilities and limitations, and managing expectations.
• Knowledge sharing and mentoring: guiding developers and Tech Leads, contributing to internal standards and Council work, raising AI literacy, and promoting conscious AI tool usage.
• Supporting presales, discovery, and estimation:evaluating technical fit, performing PoCs, supporting proposals, preparing build vs. buy vs. integrate decisions, and assessing AI feasibility and risks.
• Prioritization across multiple projects: independently managing technical priorities, identifying critical decision points and blockers, coordinating delegation, and acting on urgent AI-related anomalies.

Key deliverables:

• Architecture Design Document (ADD) and other architectural documentation. For AI components: model architecture, data flow, inference pipeline, guardrails
• Architecture Decision Records (ADR). For AI: model selection, vendor, approach justification
• System diagrams (context, container, component, deployment, integration). For AI: data flow diagrams, inference pipeline, feedback loop
• Non-Functional Requirements (NFR) specification with measurement/validation criteria. AI-specific: accuracy, latency, cost per inference, hallucination rate, drift threshold
• Technical roadmaps, risk analysis, technology evaluation or PoC documentation
• API/integration design materials, onboarding guides, estimation inputs. For AI: prompt interface, model API contract, embedding pipeline design (can be prepared by AI Tech Lead / ML Engineer Lead, but architectural approval remains with the architect)
• AI Solution Outline (if applicable): use case, model approach, data sources, inference architecture, guardrails, monitoring, human-in-the-loop design, cost model

What makes you a good fit:

• You are proactive and technically decisive. You can easily identify risks, blockers, technical debt, and situations needing architectural intervention; documents and justifies decisions while building consensus.
• AI-ready mindset: You consciously apply AI in work, make business-grounded decisions on where AI should or shouldn’t be used, and communicate usage transparently.
• Strong architectural experience: senior software engineering background with multi-component, integration-heavy system design; skilled in system design, architectural patterns, APIs, data flows, cloud/platform solutions, and operations.
• AI expertise: You have experience with LLM integration, RAG pipelines, agent architectures, model serving, vector DBs, embedding strategies, inference optimization, MLOps, guardrails, monitoring, and prompt engineering.
• You understand fairness, transparency, explainability, and accountability in AI. You are experienced in defining and measuring NFRs, including AI-specific ones.
• Leadership and collaboration: mentoring team members, facilitates decisions, manages stakeholders, communicating effectively, and driving alignment across projects and professional councils.
• Business and trade-off mindset: You have strong decision-making skills, You are able to balance technical, business, and project priorities across multiple initiatives.

Nice to have

• Experience in presales, discovery, or scoping
• Experience in PoCs, technology evaluation, roadmap planning, or high-risk migrations/integrations
• Cloud or architecture certifications (e.g. AWS, Azure, GCP, TOGAF, IASA, ArchiMate)
• Experience contributing to internal communities or standardization
• AI/ML certifications (AWS ML Specialty, Azure AI Engineer, Google Cloud ML Engineer)
• Experience in build vs. buy vs. integrate decisions in AI context

What we offer:

• Personal Growth Plan: training, education and real career growth opportunity in our Growth Program
• Stable, market-level income + Cafeteria
• Significant professional development and advancement opportunities
• Training, educational, language learning and career opportunities
• High-impact projects where your architectural decisions matter
• People-centric approach
• Creative atmosphere, balanced, cheerful work environment
• Environmentally conscious office

What you can be a part of as a colleague:

• Internal community events (team-building programs, year-end events, football, and other spontaneous activities)
• Recreation – a relaxation room with bean bags, Kinect, LEGO Mindstorms, and VIVE
• Fruit days, coffee, tea, mineral water, and everything you might need for them

Workplace: Győr and/or Budapest.

The strength of our company lies in our people-centered approach, teamwork, and quality, which fundamentally shape our company culture. This is a place where you can work with friendly geeks on super exciting projects.

And now, we are looking for you! 🙂