AI Solutions Architect

We will count on you to:

Design and govern AI solution architectures

* Work with business and technology partners to understand problems, opportunities, and constraints.

* Design, document, and communicate solution architectures for AI-enabled systems, from data ingestion through to user-facing applications.

* Ensure architectures align with enterprise standards, reference architectures, and platform strategies.

* Incorporate reuse, standardisation, and security-by-design principles into solution designs.

* Balance agility (“fail fast”) with the resilience required for enterprise production systems.

Support delivery teams

* Partner with Engineering Managers, Tech Leads, and delivery pods to guide architectural decisions throughout delivery.

* Participate in design reviews and provide architectural assurance as part of formal or informal governance processes.

* Support teams during implementation, from infrastructure setup through to production readiness, as needed.

* Enable teams to deliver solutions independently, avoiding over-prescription or unnecessary complexity.

Lead AI innovation and experimentation

* Lead targeted experimentation and technical spikes to evaluate emerging AI technologies, tools, and architectural approaches.

* Design and run proofs of concept to de-risk complex AI solutions before wider adoption.

* Translate experimentation outcomes into clear architectural guidance, patterns, and standards for delivery teams.

* Partner with Engineering Managers, Tech Leads, and the AI CoE to move successful experiments into production-ready solutions.

* Ensure innovation activities consider security, governance, cost, and operability from the outset.

Shape enterprise AI practices

* Contribute to the evolution of enterprise AI architecture patterns, standards, and guidance.

* Influence technology roadmaps by identifying opportunities for platform modernisation and component reuse.

* Participate in architecture communities of practice and knowledge sharing across the organisation.

* Bring a shift-left mindset, ensuring quality, automation, and operability are designed in early.

Cost, risk, and compliance awareness

* Support cost estimation and solution sizing during planning and budget cycles.

* Ensure AI solutions comply with governance, security, privacy, and regulatory requirements.

* Identify architectural risks and trade-offs early and communicate them clearly to stakeholders.

What you need to have:

* 3 - 4 years of relevant professional software engineering experience in production environments;

* 1 - 2 years of hands-on experience working with Large Language Models (LLMs);

* Proficiency in a modern frontend framework, ideally React with TypeScript (Angular is also acceptable);

* Hands-on experience with NestJS/NodeJS;

* Experience with or working knowledge of FastAPI or Django using Python;

* Strong understanding of RESTful APIs;

* Exposure to deploying and maintaining cloud-based systems;

* Strong communication skills in English and Romanian;

* Extensive experience designing and communicating solution architectures for enterprise systems;

* Proven ability to translate requirements into clear architectural designs and decision records;

* Experience evaluating emerging AI tools or platforms and influencing their adoption through evidence-based architectural recommendations;

* Strong experience working with Agile delivery teams and operating in fast-paced, evolving environments;

* Ability to work effectively with both technical and non-technical stakeholders across levels of seniority;

* A self-directed mindset with the ability to operate independently while collaborating across teams.

Technical experience

* Modern application architectures (e.g., microservices, API-driven systems)

* Full-stack or middleware technologies (e.g., JavaScript/TypeScript, .NET, Python)

* Cloud-native architectures and containerisation (e.g., Kubernetes)

* Identity and access management (e.g., OAuth, SAML, MFA)

* Secure DevOps and Secure by Design principles

* Data platforms (e.g., data lakes, analytics, BI tooling)

* Observability and application performance monitoring

* Familiarity with architectural documentation standards (e.g., C4, UML, ArchiMate)