Senior Engineer, AIML Platform Architect

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 require