AI Solution Architect | Senior Management

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