AI Native Solution Architect

We are looking for an experienced AI Native Solution Architect to design and scale enterprise-grade AI solutions within high-velocity, AI-native delivery pods. In this role, you will serve as a pivotal technical leader, bridging business strategy, product vision, and technical execution. You will establish architecture blueprints, select modern tech stacks, and guide cross-functional teams to build resilient, production-ready AI systems.

Responsibilities End-to-End Solution Architecture: Design scalable, secure, and resilient architectures for enterprise AI systems, covering data pipelines, model integration, deployment, and monitoring Modern AI System Design: Architect advanced AI patterns, including RAG, agentic workflows, and hybrid AI architectures using vector databases and graph frameworks Strategic Product & Tech Alignment: Translate complex business goals and product roadmaps into technical specifications, architecture blueprints, and clear delivery backlogs Technical Leadership & Mentorship: Guide engineering teams on implementation best practices, design patterns, coding standards, and system maintainability Technology Evaluation & Strategy: Lead technical discovery, vendor evaluations, framework selections, and trade-off analyses (e.g., build vs. buy, open-source vs. proprietary APIs) Cross-Functional Collaboration: Partner closely with Product Leads, Engineers, Software Architects, and Business Stakeholders to ensure seamless delivery and measurable ROI Requirements Proven Track Record: 3+ years of experience as a Solution/System Architect, Principal Engineer, or Tech Lead, with demonstrated experience delivering enterprise-scale AI solutions into production Modern AI Engineering: Deep hands-on and architectural experience with modern GenAI patterns (RAG, Multi-Agent systems, function calling, tool use) and orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel) Data & Search Infrastructure: Expertise in vector databases and enterprise search/retrieval strategy Cloud & LLMOps: Strong background in cloud platforms (AWS, Azure, or Google Cloud Platform) and operationalizing models using Familiarity with LLMOps/MLOps practices Software Engineering Core: Solid background in modern backend systems engineering, microservices, API design, and distributed systems Stakeholder Bridge: Exceptional communication skills with a proven ability to translate deep technical concepts into plain language for executive stakeholders and product leaders Pragmatic Problem Solver: Comfortable navigating rapid tech evolutions, making trade-off decisions under uncertainty, and advocating for production quality Excellent command of written and spoken English (B2+ level)