Senior AI Solution Architect

Senior AI Solution ArchitectBoston Scientific was recognized as a Glassdoor Best Place to Work in 2026, ranking No. 15 on the Top 100 list, reflecting the culture our employees experience every day. Boston Scientific is seeking an AI solution architect to join our AI Engineering team and lead the design of next-generation AI solutions across the enterprise. In this role, you will bridge the gap between complex business needs and advanced AI system design, translating business challenges into scalable, secure and compliant generative AI and agentic AI architectures. You will define end-to-end technical solution architectures for AI-powered products, including custom generative AI applications, intelligent agents, virtual assistants and reusable AI services. This role requires deep technical expertise, strong architectural judgment and the ability to influence cross-functional stakeholders.Work model, sponsorship, relocation: At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in our local office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. Relocation assistance is not available for this position at this time.Your responsibilities will include:Lead the end-to-end architecture of enterprise AI solutions, including generative AI applications, large language model-powered workflows, agentic systems and intelligent automation.Design modular and reusable AI components and services that can be leveraged across multiple platforms and business use cases.Define architectural patterns for agent orchestration, tool integration, memory management, retrieval-augmented generation and human-in-the-loop workflows.Translate business requirements into scalable, production-ready AI architectures aligned with enterprise standards.Partner with business stakeholders to understand objectives, constraints and value drivers, helping ensure measurable business impact.Collaborate with AI engineers, software engineers, data scientists and data engineers to guide implementation and maintain architectural integrity.Partner with enterprise architecture, cybersecurity, legal, privacy, quality and platform engineering teams to help ensure solutions meet regulatory, security and quality expectations.Architect secure and scalable data pipelines in partnership with data engineering teams to support AI and generative AI workloads.Evaluate and integrate technologies across Azure, AWS and Snowflake to deliver cloud-native, resilient and cost-effective solutions.Guide platform-level decisions related to model hosting, vector databases, orchestration frameworks, monitoring and MLOps/LLMOps practices.Ensure solutions are designed for performance, reliability, observability and operational excellence.Embed ethical AI, security-by-design, privacy-by-design and compliance-by-design principles into solution architectures.Support risk assessments, model reviews and required documentation for enterprise and regulated environments.Qualifications: Required qualifications: Bachelor's or master's degree in computer science, engineering, data science or a related technical field. Minimum of 5 years' experience in solution architecture, software architecture or AI/ML engineering, including recent hands-on work in generative AI. Proven experience designing and deploying large language model-based solutions, including retrieval-augmented generation, prompt engineering and model integration. Experience in health care, life sciences or another highly regulated industry. Strong understanding of cloud-native architectures in Azure and/or AWS and modern data platforms such as Snowflake. Demonstrated experience working in enterprise-scale, regulated environments with security, compliance and quality requirements. Demonstrated ability to communicate complex technical concepts clearly to technical and nontechnical audiences.Preferred qualifications: Proven experience with agentic AI frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI or similar technologies. Familiarity with vector databases, embedding strategies and search optimization techniques. Hands-on experience with MLOps/LLMOps, including model monitoring, evaluation and lifecycle management. Proven experience defining reference architectures, design patterns and reusable AI platforms.