Senior Principal AI Architect

MiniMed•Johns Creek, GA

About The Position

MiniMed.com is seeking a Senior Principal AI Architect to lead the architecture, design, and implementation of AI-powered solutions that accelerate our ecommerce transformation. This role will shape how AI is applied across the digital commerce ecosystem — from customer-facing experiences and internal decision support to workflow automation, knowledge retrieval, personalization, and scalable platform capabilities. The ideal candidate is a strategic, hands-on technology leader who can connect business priorities with modern AI architecture and enterprise-grade execution. They bring deep experience designing secure, scalable AI solutions, integrating large language models and intelligent agents into enterprise environments, and building the underlying data, cloud, and governance foundations required to support long-term adoption. This person should be equally comfortable advising senior stakeholders, defining architecture, and guiding technical teams through delivery.

Requirements

Requires a Bachelors degree and minimum of 10 years of relevant experience, or advanced degree with a minimum of 8 years of relevant experience.
8+ years of experience in software engineering, solution architecture, enterprise platforms, or AI/ML systems design
Strong experience architecting and delivering enterprise AI solutions that connect models, data, workflows, and business processes
Deep familiarity with modern AI patterns such as LLM applications, autonomous or agentic workflows, RAG, semantic search, embeddings, and enterprise knowledge system
Experience designing secure, scalable cloud-native architectures across modern platforms such as Azure, GCP, or AWS
Strong understanding of enterprise data ecosystems, including data governance, access controls, structured and unstructured data, and scalable retrieval architectures
Experience partnering across engineering, product, security, and business teams to align technical direction with organizational priorities
Ability to move fluidly from strategy and architecture to hands-on solution guidance and implementation support
Strong communication and stakeholder management skills, with the ability to explain complex technical concepts in business terms
Experience establishing engineering standards and delivery practices including CI/CD, DevSecOps, Infrastructure as Code, and operational reliability
Proven ability to design solutions that balance innovation, security, scalability, and speed to value

Nice To Haves

Experience supporting digital commerce, ecommerce transformation, customer engagement platforms, or AI-enabled personalization initiatives
Experience building AI solutions that improve both customer-facing journeys and internal business operations
Familiarity with frameworks and tools such as Azure OpenAI, OpenAI, Gemini, Semantic Kernel, LangChain, vector databases, and enterprise search platforms
Experience building or advising on autonomous agents, workflow orchestration, and multi-step AI decision systems
Experience with data platforms, data governance, and modern architectural patterns such as lakehouse, data mesh, or self-service data environments
Experience in healthcare, regulated industries, or environments where security, privacy, and compliance are critical
Background leading global engineering teams or large-scale enterprise transformation initiatives

Responsibilities

Lead architecture for AI solutions supporting MiniMed.com’s ecommerce transformation, including customer experience, internal enablement, automation, personalization, and intelligent decision-support use cases
Define scalable AI solution patterns across multiple initiatives, including LLM-powered applications, agentic workflows, retrieval-augmented generation (RAG), search, recommendations, and knowledge management experiences
Translate business goals, operational needs, technical constraints, and data considerations into practical AI architectures that deliver measurable value
Design secure, cloud-native AI systems that integrate enterprise data, APIs, digital platforms, and model orchestration frameworks
Partner with product, engineering, data, security, and business stakeholders to define roadmaps, prioritize use cases, and align architecture decisions with transformation goals
Establish best practices for AI engineering, including observability, governance, model lifecycle management, security, and responsible deployment
Guide the design of data pipelines, vector-based retrieval systems, semantic search, and knowledge interfaces that improve access to information across teams and workflows
Evaluate emerging AI tools, platforms, and frameworks to determine where they can accelerate delivery or unlock new capabilities
Drive technical quality and scalability through strong engineering discipline, including DevSecOps, CI/CD, Infrastructure as Code, and resilient platform design
Influence senior stakeholders through clear architectural storytelling, solution framing, tradeoff analysis, and implementation planning