Xebia
Technology
AIArchitect
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Architect at Xebia. Skills: AI Architect, Databricks, AI agents, LLM. Lead discovery workshops. Identify high-value workflows”
What You'll Achieve.
Reduce manual analytical effort; Improve decision-making quality; Analyst time reduction; Query accuracy; Turnaround time reduction; Achieve production deployment
Industry & Context.
Root cause analysis
Work permit required
What They're Looking For.
Must Have
Experience in data analytics, Experience in data engineering, Experience in analytics platform delivery, Hands-on experience with Databricks, Experience with Unity Catalog, Experience with governed data access, Experience working with SQL, Experience with analytical data models, Solid understanding of data governance, Solid understanding of security, Solid understanding of access control, Experience designing AI/LLM solutions, Experience working with AI/LLM solutions, Ability to translate business workflows, Stakeholder communication skills, Experience working with business analysts, Experience with semantic layers, Experience with business glossaries, Experience with metadata management, Experience benchmarking AI outputs, Exposure to enterprise analytics environments, Experience working in pilot engagements, Experience leading POC engagements, Work from European Union region, Work permit required
Nice to Have
Experience with automated reporting, Experience with anomaly detection, Knowledge of cost monitoring, Knowledge of cost optimization, Experience defining operating models, Background in large-scale organizations, Consulting experience in analytics, Consulting experience in AI transformation, Advisory experience in analytics, Advisory experience in AI transformation
What You'll Do.
Lead discovery workshops
Identify high-value workflows
Prioritize high-value workflows
Define success metrics
Audit Databricks assets
Audit Databricks pipelines
Audit governance constraints
Prepare AI-ready datasets
Evaluate AI agent performance
Support pilot deployment
Measure business impact
Contribute to ROI recommendations
Contribute to scaling recommendations
Define production deployment
How You'll Work.
Team & Collaboration
Working with business analysts
Communication Scope
Stakeholder communication
Process & Methodology
Roadmap planning
Full Job Description
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, we help organizations design and build scalable products, platforms, and data-driven solutions. We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. About the role: The project focuses on augmenting business and data analysts using AI agents built on top of the Databricks ecosystem. The goal is to accelerate insight generation, reduce manual analytical effort, and improve decision-making quality across Operations, Finance, and Digital domains. You will be: leading discovery workshops to define what “analyst augmentation” means in practical, measurable terms. identifying and prioritizing high-value analyst workflows (OPS, Finance, Digital), defining success metrics such as analyst time reduction, query accuracy, and turnaround time, auditing existing Databricks data assets, pipelines, and governance constraints, preparing AI-ready datasets, including metadata, documentation, and semantic layers, designing and prototyping AI agents that replicate or support analyst tasks, implementing agents using Databricks tools (e.g., Genie, Unity Catalog), defining and implementing guardrails (security, cost control, hallucination prevention), evaluating AI agent performan
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