Senior AI Solution Architect

About the role

Boston Scientific is seeking a Senior AI Solution Architect to join their AI Engineering team and lead the design of next-generation AI solutions across the enterprise.

Responsibilities

• 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 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, ensuring measurable business impact.
• Collaborate with AI engineers, software engineers, data scientists and data engineers to guide implementation and ensure architectural integrity.
• Partner with enterprise architecture, cybersecurity, legal, privacy, quality and platform engineering teams to 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 all solution architectures.
• Support risk assessments, model reviews and required documentation for enterprise and regulated environments.

Requirements

• Minimum 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.
• Previous background in healthcare, life sciences or other highly regulated industries.
• 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.
• Preferred hands-on experience with MLOps/LLMOps, including model monitoring, evaluation and lifecycle management.
• Proven experience defining reference architectures, design patterns and reusable AI platforms.