Principal AI Solutions Architect

Our requirements

• Extensive experience in enterprise software architecture, AI solution architecture, or a similar senior technical leadership role.
• Proven track record of designing and owning complex, production-grade software systems, including architecture, integrations, reusable components, and non-functional requirements.
• Strong software engineering background and the ability to actively design, develop, test, review, and maintain production-quality code.
• Experience with modern software engineering practices, including version control, code reviews, automated testing, and CI/CD.
• Strong technical foundation in at least one modern programming ecosystem, such as TypeScript/Node.js, Python, Java, or .NET.
• Experience designing scalable, maintainable, and reusable software architectures.
• Knowledge of TypeScript and Node.js is an advantage but is not mandatory.
• Extensive hands-on experience with modern AI technologies, platforms, and tools, with a broad understanding of the current AI ecosystem.
• Proven experience designing, building, and deploying LLM-powered solutions used in real production environments.
• Strong understanding of agentic AI architectures and patterns, including:
• AI agents and tool use/function calling.
• Model Context Protocol (MCP).
• Prompt engineering and context engineering.
• Structured instruction design.
• Agent orchestration and workflow automation.
• Experience evaluating, selecting, and integrating AI platforms and tools based on specific business and technical requirements.
• Practical experience building evaluation frameworks for AI systems, including test cases, measurable acceptance criteria, and regression testing.
• Ability to troubleshoot AI-powered workflows and systematically improve the reliability and quality of non-deterministic outputs.
• Strong understanding of when AI is appropriate and when conventional software engineering is a more suitable solution.
• Experience defining architecture standards, reusable components, and engineering practices across multiple projects or teams.
• Ability to make strategic technology decisions and evaluate the trade-offs between different platforms and architectural approaches.
• Understanding of enterprise security, data protection, and compliance requirements.
• Experience working with sensitive business data is highly desirable.
• Proven experience working directly with external clients, senior executives, and business stakeholders.
• Strong consulting and presales background, including solution design, project scoping, effort estimation, and presenting technical proposals.
• Ability to translate complex and ambiguous business requirements into clear technical architectures and executable implementation plans.
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical audiences.
• A product-oriented mindset, focusing on business value, user adoption, and measurable outcomes rather than technology alone.
• Ability to work independently in environments with evolving requirements and limited initial specifications.
• Proven experience providing technical leadership to engineering teams, including architectural guidance, code reviews, and technical decision-making.
• Ability to influence technical direction and establish engineering standards.
• Excellent problem-solving and decision-making skills.
• English proficiency at C1 level or higher, with the ability to independently lead technical and business discussions with international clients.
• Availability to work in alignment with the client's business hours in the US and Western Europe.
• Willingness to remain actively involved in software development rather than focusing exclusively on management or documentation.

Optional

• Seniority: Enterprise Architect / Principal-level AI Engineer.
• Role type: Hands-on technical leadership.
• Key focus: Enterprise AI architecture, AI engineering, technical consulting, and presales.
• Working model: Availability aligned with US and Western European business hours.

Your responsibilities

• Define and own the overall technical architecture of enterprise AI solutions, including shared components, reusable capabilities, integration patterns, and deployment strategies.
• Establish architectural standards and best practices for AI solution development, including versioning, testing, evaluation, and release management.
• Make and justify platform-level technology decisions, including AI frameworks, agent orchestration platforms, integration architectures, and build-versus-buy strategies.
• Define architectural principles and quality standards for reusable AI components and ensure compliance through technical reviews.
• Provide technical leadership and guidance to a team of engineers, including code reviews, architectural decisions, and technical problem-solving.
• Ensure solutions meet enterprise requirements related to scalability, reliability, security, data protection, and compliance.
• Design, build, test, and maintain production-ready AI solutions and reusable AI capabilities.
• Remain hands-on throughout the development lifecycle, writing and reviewing code and contributing directly to implementation.
• Develop AI-powered workflows and agentic solutions using modern LLM technologies, orchestration frameworks, and tool integration patterns.
• Design evaluation frameworks, including test datasets, acceptance criteria, regression testing, and quality assurance processes for non-deterministic AI systems.
• Investigate and resolve issues related to AI agent behaviour, workflow execution, and output quality.
• Integrate AI solutions with enterprise platforms, business applications, and data sources.
• Ensure that AI solutions are reliable, maintainable, and suitable for production use.
• Work directly with senior client stakeholders, including CTOs, enterprise architects, and business leaders, to identify business needs and define technical solutions.
• Assess business processes to determine where AI can deliver measurable value and where traditional software solutions are more appropriate.
• Lead technical discovery sessions and translate ambiguous business requirements into clear architectural designs and implementation plans.
• Shape solution proposals, define project scope, estimate implementation effort, and present technical approaches to clients.
• Communicate architectural decisions, technical trade-offs, and business benefits to both technical and non-technical audiences.
• Participate actively in presales activities, supporting the development and presentation of AI solution proposals.
• Take ownership of business outcomes, focusing on user adoption, operational efficiency, and measurable improvements.
• Monitor developments in AI technologies, tools, and platforms and assess their applicability to enterprise use cases.
• Identify opportunities to introduce new AI capabilities and improve existing solutions.
• Lead pilot implementations, gather feedback from end users, and drive iterative improvements.
• Establish standards for developing, reviewing, versioning, and releasing reusable AI components.
• Promote engineering excellence and continuous improvement across the AI development lifecycle.

About the project

This is how we organize our work

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