Sr Engineer, AIML Platform Architect

Job description

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. 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 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.