About the Role
We are looking for a senior, customer-facing AI Solutions Architect to help customers build production-grade AI and agentic solutions on top of their governed data using semantic models, BI platforms, and local inference infrastructure as building blocks. You will own customer engagements end-to-end: from discovery and solution design through prototyping, delivery, and production handoff. You will combine strategic consulting with hands-on implementation, working directly with customers and internal teams to move ideas from discovery and prototype to production.
This is not a traditional dashboard-building role. You will work at the intersection of BI, AI, semantic modeling, customer strategy, and hands-on solution delivery. You will help shape how enterprise customers use AI inside governed analytics experiences, turning trusted data into intelligent, interactive, and production-ready analytical workflows.
What You Will Do:
Design and deliver AI-powered analytics solutions and applications on GoodData Cloud, combining modern BI, governed semantic models, LLMs, APIs, and custom workflows.
Design and prototype agentic solutions with customers. Translate business requirements into agent flows, tool definitions, and integration patterns using GoodData's agentic platform and MCP/A2A protocols. Guide customers from POC to production-ready architecture.
Own customer-facing AI and analytics initiatives end-to-end, from problem definition and discovery through solution design, prototyping, implementation, and production delivery.
Work directly with US-market customers as a trusted technical advisor. Lead deep-dive discussions on business requirements, reporting roadmaps, workflows, target personas, product strategy, and measurable business outcomes.
Contribute to AI architecture decisions, including build / buy / partner evaluations, TCO, governance, scalability, compliance, and secure multi-tenant design.
Implement AI capabilities using LLM APIs, prompt design, structured outputs, embeddings, vector search, REST APIs, scripting, and GoodData Cloud extensibility.
Work with technologies such as Python, modern data platforms, semantic layers, and vector databases such as Qdrant to prototype and deliver customer-specific AI analytics solutions.
Coordinate technical solution design across consulting, product, sales, and support teams to ensure customer needs are translated into scalable, maintainable implementations.
Help build the team's delivery capability. Contribute reusable solution templates, document delivery methodology for agentic projects, and share learnings from customer engagements to raise the bar across the team.
Here's what will help you succeed:
Proven customer-facing consulting experience with the ability to understand customer business processes, data architectures, and analytical requirements, identify opportunities for process optimization through analytics and AI, and translate technical trade-offs into practical solutions that maximize business value.
Technical fluency sufficient to build and deliver. Comfortable directing AI coding tools (Cursor, Claude, Copilot) to prototype integrations and agentic workflows, reading and modifying the output, and understanding what's happening under the hood. Solid grasp of data modeling, semantic layers, and modern ELT patterns.
Ability to integrate AI capabilities into real applications or customer solutions using APIs, prompt design, structured outputs, function / tool calling, retrieval, embeddings, vector search, or agentic workflow patterns.
Practical understanding of enterprise AI concerns such as hallucination management, output evaluation, data governance, permissions, privacy, security, user trust, and responsible use of customer data.
Self-directed and able to manage multiple customer engagements with minimal supervision while collaborating effectively across internal teams.
Fluent written and spoken English.
And a few nice-to-haves:
Experience with GoodData, embedded analytics, headless BI, semantic layers, or multi-tenant analytics platforms.
Experience with vector databases such as Qdrant. Experience with RAG patterns, LLM evaluation, AI agents, observability, guardrails, or production-grade AI workflows.
Experience with MCP (Model Context Protocol) or A2A (Agent-to-Agent) protocols for connecting AI agents to tools and external systems.
Experience with self-hosted or local inference — running open-weight models using frameworks such as vLLM, TEI, Ollama, or similar inference servers.
Experience designing or running LLM evaluation pipelines — comparing model outputs, measuring task-specific performance, and establishing production readiness criteria.
About GoodData.AI
At GoodData.AI, we’re building the future of agentic AI for data intelligence.
Our full-stack data intelligence platform empowers companies to turn raw data into real business impact — from agentic AI apps to embedded analytics that scale securely and seamlessly. With GoodData.AI, organizations don’t just analyze data, they monetize it.
But what really sets GoodData.AI apart is our mission: to put intelligence wherever decisions are made. That’s why we’ve built a platform that’s AI-native, composable, and built for the way modern enterprises work.
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