Job description

Additional Locations: N/A Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions. Boston Scientific is seeking an AI Platform Architect to design, evolve, and operate a shared enterprise AI platform used by multiple projects and product teams across the organization. This is a senior technical individual contributor role focused on enterprise AI platform capabilities, governance, security, observability, evaluation, MLOps/LLMOps, CI/CD, multi-cloud and multi-region integration, operational support, and reusable enterprise tooling. The role will define platform standards and also contribute hands-on where needed to integrate services, automate lifecycle processes, support production environments, and resolve complex platform issues. Key Responsibilities: AI Platform Architecture & Capabilities • Design and evolve a shared enterprise AI platform that supports multiple projects, business units, and product teams. • Define reusable platform capabilities for GenAI, RAG, model access, orchestration, prompt management, evaluation, observability, governance, and security. • Build and maintain AI/model gateways, service catalogs, reusable APIs, SDKs, platform integrations, and enterprise self-service tools . • Establish platform standards and reference patterns that enable teams to consume approved AI capabilities consistently and securely. • Evaluate emerging technologies and mature proven approaches into scalable enterprise platform capabilities. MLOps, LLMOps & CI/CD • Build and maintain CI/CD pipelines for AI services, models, prompts, agents, configurations, and platform components. • Implement MLOps/LLMOps practices for model lifecycle management, versioning, evaluation, deployment, rollback, monitoring, and release governance. • Develop automated evaluation frameworks for quality, grounding, safety, latency, reliability, and model performance. • Implement enterprise observability across model calls, prompts, integrations, token usage, cost, logs, traces, and platform health. • Standardize production-readiness, deployment, and promotion patterns across projects, environments, regions, and cloud platforms. Multi-Cloud Integration, Enterprise Tools & Support • Design and operate AI platform capabilities across Azure, AWS, and Snowflake , with support for multi-cloud and multi-region deployment patterns. • Build reusable enterprise integrations connecting AI services with internal applications, data platforms, APIs, identity services, and approved third-party platforms. • Develop and support shared enterprise tools, APIs, automation, and platform services that can be reused across multiple projects. • Provide hands-on production support , including incident triage, root-cause analysis, performance tuning, troubleshooting, and operational improvements. • Design for scalability, reliability, resilience, regional availability, security, cost efficiency, and maintainability across platform services and integrations. Security, Governance & Platform Standards • Embed security-by-design, privacy-by-design, Responsible AI, and compliance-by-design into platform capabilities. • Implement IAM, audit logging, access controls, traceability, data protection, AI security guardrails, and policy enforcement. • Define reusable reference architectures, APIs, SDKs, design patterns, ADRs, governance controls, and platform standards. • Partner with Cybersecurity, Privacy, Quality, Enterprise Architecture, and engineering teams to translate governance requirements into practical technical controls. Technical Leadership • Act as a senior technical contributor , helping project teams adopt and integrate shared AI platform capabilities. • Provide technical guidance through design reviews, code reviews, reference implementations, and mentoring. • Translate project and business requirements into reusable, scalable platform capabilities while remaining engaged in implementation and support. Required Qualificaitons: • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field. • Strong experience designing and operating enterprise platforms used by multiple projects or product teams. • Strong hands-on Python and API integration skills, with experience building reusable platform services and enterprise tools. • Proven experience with GenAI / LLM platform capabilities , including RAG, model integration, evaluation, observability, and lifecycle management. • Working knowledge of Model Context Protocol (MCP) , including MCP-based integration patterns, tool connectivity, security, and governance. • Strong experience with MLOps/LLMOps, CI/CD, governance, security, production support, release management, and AI lifecycle automation . • Experience with Azure, AWS, and Snowflake AI/data platforms, cloud-native architecture, enterprise integration, and production operations. • Experience with LangChain / LangGraph for AI orchestration and platform integration patterns. • Experience designing for multi-cloud and multi-region deployment, resilience, and operational support. • Ability to move independently from technical design through implementation, deployment, troubleshooting, and operationalization . • Strong understanding of secure, scalable software architecture and engineering fundamentals. • Willingness to work with some overlap with US business hours to collaborate with US-based teams and stakeholders. Preferred Qualifications: • Experience with MCP, Agent Mesh, tool gateways, agent registries, model gateways, and enterprise AI service catalogs . • Experience with vector databases, Snowflake Cortex, and Kubernetes/EKS/ECS. • Experience developing reusable SDKs, platform APIs, shared services, or internal developer platforms. • Knowledge of AI evaluation, observability, Responsible AI, AI cost optimization, and platform FinOps. • Experience in healthcare, life sciences, medical technology, or another regulated industry. Requisition ID: 633516 Boston Scientific transforms lives through innovative medical technologies that improve the health of patients around the world. As a global medical technology leader for more than 45 years, we advance science for life by providing a broad range of high-performance solutions that address unmet patient needs and reduce the cost of healthcare. Our portfolio of devices and therapies helps physicians diagnose and treat complex cardiovascular, respiratory, digestive, oncological, neurological and urological diseases and conditions. Learn more at  www.bostonscientific.com  and follow us on  LinkedIn .

Requirements

Must-haves

Additional Locations: N/A
Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance
AI Platform Architecture & Capabilities
Design and evolve a shared enterprise AI platform that supports multiple projects, business units, and product teams.
Define reusable platform capabilities for GenAI, RAG, model access, orchestration, prompt management, evaluation, observability, governance, and security.
Build and maintain AI/model gateways, service catalogs, reusable APIs, SDKs, platform integrations, and enterprise self-service tools .
Establish platform standards and reference patterns that enable teams to consume approved AI capabilities consistently and securely.
Evaluate emerging technologies and mature proven approaches into scalable enterprise platform capabilities.
MLOps, LLMOps & CI/CD
Build and maintain CI/CD pipelines for AI services, models, prompts, agents, configurations, and platform components.
Implement MLOps/LLMOps practices for model lifecycle management, versioning, evaluation, deployment, rollback, monitoring, and release governance.
Develop automated evaluation frameworks for quality, grounding, safety, latency, reliability, and model performance.
Implement enterprise observability across model calls, prompts, integrations, token usage, cost, logs, traces, and platform health.
Standardize production-readiness, deployment, and promotion patterns across projects, environments, regions, and cloud platforms.
Multi-Cloud Integration, Enterprise Tools & Support
Design and operate AI platform capabilities across Azure, AWS, and Snowflake , with support for multi-cloud and multi-region deployment patterns.
Build reusable enterprise integrations connecting AI services with internal applications, data platforms, APIs, identity services, and approved third-party platforms.
Develop and support shared enterprise tools, APIs, automation, and platform services that can be reused across multiple projects.

Nice-to-haves

Experience with MCP, Agent Mesh, tool gateways, agent registries, model gateways, and enterprise AI service catalogs .
Experience with vector databases, Snowflake Cortex, and Kubernetes/EKS/ECS.
Experience developing reusable SDKs, platform APIs, shared services, or internal developer platforms.
Knowledge of AI evaluation, observability, Responsible AI, AI cost optimization, and platform FinOps.
Experience in healthcare, life sciences, medical technology, or another regulated industry.
Requisition ID: 633516

Application prep

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Keywords to mirror

aici/cdazureawspythonapillmkubernetes

Platform: career-feeds
Effort: medium