- Work model
- Hybrid
- Experience
- 5+ years
- Employment
- Not specified
- Compensation
- Not disclosed
- Technology signal
- 13 tags
Technology context
13Parsed from the vacancy text; ordered by relevance to this role.
AIMachine LearningRAGCloudSnowflakeDatabricksPower BIAI ArchitectureCloud Data ServicesData Solution ArchitectureLife SciencesLLMOpsMachine Learning Engineering
Full listing
Role 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