AI Solutions Architect

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.

Responsibilities

• 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.
• 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.
• AI solutions comply with governance, security, privacy, and regulatory requirements.
• Identify architectural risks and trade-offs early and communicate them clearly to stakeholders.

Requirements

• 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)