AI Solution Architect | Senior Management
Hybrid, Polska
Description
We are looking for an experienced AI Solution Architect responsible for designing scalable, enterprise-grade AI and data solutions within AI-native delivery pods. In this role, you will act as a pivotal bridge—ensuring perfect alignment between business objectives, product requirements, and technical architecture while driving the implementation of modern Data & AI platforms. Responsibilities End-to-End Architecture: Define and design comprehensive, scalable architecture for enterprise AI and data solutions AI-Native Innovations: Design advanced AI-native patterns including RAG, agentic workflows, and robust data pipelines Bridge Product & Tech: Translate complex business objectives and product requirements into clear, executable technical designs Engineering Mentorship: Guide and mentor engineering teams on implementation standards, coding guidelines, and best practices Governance & Principles: Define, implement, and enforce strict architecture principles and data governance frameworks Cross-functional Collaboration: Partner closely with Product Leads, Data Architects, and Engineers to ensure seamless delivery Quality Assurance: Guarantee the highest standards of scalability, high performance, and robust security across all solutions Tech Selection: Support and lead technology evaluation, selection, and platform-level decisions Requirements AI Engineering: Deep understanding of patterns like RAG, agentic workflows, and hybrid AI systems using frameworks such as LangChain, Semantic Kernel, and LlamaIndex AI / ML Operations: Proven experience with MLOps / LLMOps pipelines, evaluation frameworks, feedback loops, vector databases, and embedding pipelines Data Platforms: Hands-on familiarity with Databricks and Snowflake, ETL/ELT pipelines (both batch and streaming processing), data modeling, and semantic layers Analytics & Visualization: High-level awareness of business intelligence tools like Power BI, Qlik, and Qlik Sense Proven Experience: Solid track record as a Solution Architect or Lead Engineer specializing in enterprise-grade Data & AI systems Stakeholder Management: Strong communication skills with the ability to articulate complex technical concepts to non-technical business stakeholders and Product Leads Security & Scale Mindset: A proactive focus on enterprise-grade security, scalability, performance tuning, and data governance
Requirements
AI Engineering: Deep understanding of patterns like RAG, agentic workflows, and hybrid AI systems using frameworks such as LangChain, Semantic Kernel, and LlamaIndex
AI / ML Operations: Proven experience with MLOps / LLMOps pipelines, evaluation frameworks, feedback loops, vector databases, and embedding pipelines
Data Platforms: Hands-on familiarity with Databricks and Snowflake, ETL/ELT pipelines (both batch and streaming processing), data modeling, and semantic layers
Analytics & Visualization: High-level awareness of business intelligence tools like Power BI, Qlik, and Qlik Sense
Proven Experience: Solid track record as a Solution Architect or Lead Engineer specializing in enterprise-grade Data & AI systems
Stakeholder Management: Strong communication skills with the ability to articulate complex technical concepts to non-technical business stakeholders and Product Leads
Security & Scale Mindset: A proactive focus on enterprise-grade security, scalability, performance tuning, and data governance
Responsibilities
End-to-End Architecture: Define and design comprehensive, scalable architecture for enterprise AI and data solutions
AI-Native Innovations: Design advanced AI-native patterns including RAG, agentic workflows, and robust data pipelines
Bridge Product & Tech: Translate complex business objectives and product requirements into clear, executable technical designs
Engineering Mentorship: Guide and mentor engineering teams on implementation standards, coding guidelines, and best practices
Governance & Principles: Define, implement, and enforce strict architecture principles and data governance frameworks
Cross-functional Collaboration: Partner closely with Product Leads, Data Architects, and Engineers to ensure seamless delivery
Quality Assurance: Guarantee the highest standards of scalability, high performance, and robust security across all solutions
Tech Selection: Support and lead technology evaluation, selection, and platform-level decisions
Seniority
Senior Management
Keywords / Skills