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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. - Solid 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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.