Agentic AI Architect

You will be:
• defining the target architecture for an enterprise agentic AI foundation, separating shared capabilities from those specific to data management and data delivery use cases,
• assessing existing AI initiatives, components and platforms for maturity, business value, reuse potential, scalability, cost and control impact, and recommending what to reuse, adapt, buy, build or retire,
• benchmarking the client's approach against mature internal agentic patterns and current market practice,
• evaluating architecture and platform options across agent orchestration, APIs and MCP, data access and grounding, evaluation, observability and AgentOps/LLMOps,
• building non-functional requirements into the design from the start: security, privacy, resilience, performance, auditability, explainability, access control, human oversight and cost,
• preparing objective buy-versus-build recommendations for key components, covering total cost, speed, skills and vendor lock-in,
• shaping a lightweight lifecycle that takes team-level Copilot experiments from intake and assessment through assurance and promoaion to reusable, industrialized capabilities,
• building the proof of concept hands-on: agents, integrations, evaluation pipelines, tests and NFR evidence on representative workloads, with a clear plan for scale,
• working with data, technology, AI risk and business stakeholders in weekly reviews, demos and decision sessions,
• transferring knowledge throughout the engagement through pairing, architecture and code walkthroughs, runbooks and decision records, so client teams can continue independently.

Your profile:
• 7+ years of professional experience in software, data or AI engineering, including recent work as a solution architect, AI architect or lead engineer,
• hands-on experience designing and delivering LLM-based and agentic solutions beyond the prototype stage,
• strong Python skills and practical experience with agent orchestration, tool integration via APIs and MCP, and retrieval and grounding on enterprise data,
• experience with evaluation, observability and lifecycle management of AI systems (AgentOps/LLMOps),
• a solid understanding of data management and data delivery, such as metadata, data quality, lineage, governance, pipelines and data products,
• experience in regulated environments where security, privacy, auditability and AI risk requirements shape the architecture,
• a track record of turning prototypes into reusable, controlled and supportable capabilities,
• the ability to assess commercial products against open-source and build options objectively, including total cost of ownership and lock-in,
• confidence with senior technical and business stakeholders, and the ability to turn analysis into decision-ready recommendations,
• very good English, spoken and written.
Work from the European Union region and a work permit are required.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision