AI Architect / AI Engineer (AWS & Data Agents)

AI Architect / AI Engineer (AWS & Data Agents) Fortune 500 Electronics manufacturer Remote Role About the Role We are seeking a hands-on AI Architect / AI Engineer to help scale our enterprise AI capabilities across the organization. This role will sit at the intersection of AI architecture, data engineering, and generative AI implementation, helping transition traditional BI and analytics workflows into AI-driven solutions. You will work closely with AI Product Managers, Data Teams, and Cloud Engineering to design and build data agents, LLM-powered applications, and AI services on AWS. The role requires both architectural leadership and hands-on development, including building proofs of concept (POCs) and guiding implementation frameworks used by nearshore engineering teams. This position plays a critical role in shaping our enterprise AI architecture strategy, ensuring scalable solutions aligned with AWS best practices. Key Responsibilities: AI Architecture & Strategy • Define and implement enterprise AI architecture patterns for generative AI and intelligent data agents. • Translate business vision and AI product requirements into scalable technical architectures. • Ensure solutions align with AWS cloud architecture standards and best practices. • Design frameworks for deploying and orchestrating LLM-based systems across the enterprise. Data Agent & AI Development • Design and build AI-powered data agents that interact with enterprise data systems. • Develop architectures that enable natural language access to business intelligence and reporting systems. • Lead development of Model Context Protocol (MCP) capabilities and intelligent agent frameworks. • Implement AI solutions that integrate with enterprise data platforms. Proof of Concept Development • Be hands-on with code, developing POCs to validate architectures and accelerate delivery. • Prototype AI capabilities using AWS generative AI services and modern AI frameworks. • Establish reference architectures and reusable frameworks for engineering teams. Data & Analytics Integration • Enable the transition from traditional BI reporting to AI-driven analytics. • Integrate AI solutions with enterprise data platforms including: • Snowflake • Data transformation layers • Semantic data models • Design systems that support financial reporting, sales reporting, and operational analytics use cases. Cross-Team Collaboration • Partner with AI Product Managers to translate product vision into scalable architectures. • Work closely with nearshore AI engineers and data teams to implement solutions. • Provide technical leadership and mentorship to engineering teams building AI applications. Required Qualifications: • 8+ years experience in software engineering, data engineering, or cloud architecture • 3+ years working with AI / ML systems or generative AI architectures • Strong experience with AWS cloud architecture • Hands-on experience building LLM-powered applications or AI agent systems • Experience designing enterprise data architectures • Ability to move between high-level architecture and hands-on development Core Technical Skills: Cloud & Infrastructure • Amazon Web Services (AWS) • Experience with Amazon Bedrock • Serverless and microservice architectures • Cloud-native AI deployment patterns AI & LLM Systems • Generative AI architectures • LLM orchestration frameworks • AI agent design patterns • Model Context Protocol (MCP) implementations • Prompt engineering and AI pipelines Data Platforms • Snowflake • Data transformation tools such as dbt • Semantic data layers • Enterprise data modeling Engineering • Python or similar backend language • API and microservice development • Data pipelines and distributed systems Preferred Qualifications • Experience implementing enterprise AI platforms at scale • Background in consumer electronics or manufacturing • Experience with financial reporting and sales analytics systems • Familiarity with modern AI agent frameworks and orchestration tools • Strong understanding of enterprise BI ecosystems and semantic layers What Success Looks Like • Successfully architecting and deploying scalable AI agent frameworks on AWS • Enabling business teams to move from static BI dashboards to AI-driven insights • Delivering POCs that evolve into production AI capabilities • Establishing a repeatable framework for enterprise AI development