Sr Engineer, AIML Platform Architect

Job Summary

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.

Responsibilities

• AI Platform Architecture Capabilities: Design and evolve a shared enterprise AI platform that supports multiple projects, business units, and product teams.
• AI Platform Architecture Capabilities: Define reusable platform capabilities for GenAI, RAG, model access, orchestration, prompt management, evaluation, observability, governance, and security.
• AI Platform Architecture Capabilities: Build and maintain AI/model gateways, service catalogs, reusable APIs, SDKs, platform integrations, and enterprise self-service tools.
• AI Platform Architecture Capabilities: Establish platform standards and reference patterns that enable teams to consume approved AI capabilities consistently and securely.
• AI Platform Architecture Capabilities: 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.
• MLOps, LLMOps CI/CD: Implement MLOps/LLMOps practices for model lifecycle management, versioning, evaluation, deployment, rollback, monitoring, and release governance.
• MLOps, LLMOps CI/CD: Develop automated evaluation frameworks for quality, grounding, safety, latency, reliability, and model performance.
• MLOps, LLMOps CI/CD: Implement enterprise observability across model calls, prompts, integrations, token usage, cost, logs, traces, and platform health.
• MLOps, LLMOps CI/CD: 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.
• Multi-Cloud Integration, Enterprise Tools Support: Build reusable enterprise integrations connecting AI services with internal applications, data platforms, APIs, identity services, and approved third-party platforms.
• Multi-Cloud Integration, Enterprise Tools Support: Develop and support shared enterprise tools, APIs, automation, and platform services that can be reused across multiple projects.
• Multi-Cloud Integration, Enterprise Tools Support: Provide hands-on production support, including incident triage, root-cause analysis, performance tuning, troubleshooting, and operational improvements.
• Multi-Cloud Integration, Enterprise Tools Support: 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.
• Security, Governance Platform Standards: Implement IAM, audit logging, access controls, traceability, data protection, AI security guardrails, and policy enforcement.
• Security, Governance Platform Standards: Define reusable reference architectures, APIs, SDKs, design patterns, ADRs, governance controls, and platform standards.
• Security, Governance 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.
• Technical Leadership: Provide technical guidance through design reviews, code reviews, reference implementations, and mentoring.
• Technical Leadership: Translate project and business requirements into reusable, scalable platform capabilities while remaining engaged in implementation and support.

Required Qualifications

• 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.