Agentic AI Architect

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Senior0Full-time

#456891·Dodano miesiąc temu·3

Źródło: Xebia

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Tech Stack / Keywords

LLMagentic AIagent orchestrationAPIsMCPPythonAgentOpsLLMOpsAI architecture

Firma i stanowisko

Xebia is a global AI-first, digital transformation, and engineering partner with over 25 years of experience and a team of 5,000 professionals across 16 countries. The company specializes in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products. In the CEE region, Xebia has nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for major clients, working with technologies including AWS, Azure, GCP, Databricks, and Snowflake.

Wymagania

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).
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.
Track record of turning prototypes into reusable, controlled, and supportable capabilities.
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 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.

Obowiązki

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

Inne informacje

Work from the European Union region and a work permit are required.

Xebia sp. z o.o.

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