AI Solutions Architect.

The Role

We are seeking an experienced AI Solutions Architect to partner with engineering, product, and business teams to design and guide the delivery of enterprise-grade, AI-enabled solutions. This is a senior individual contributor role responsible for solution-level architecture across AI initiatives, ensuring designs align with enterprise standards, security requirements, and long-term platform direction.

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)