Solution Architect - Senior Staff Engineer

We Are Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. You Are You have spent years building platforms that bridge simulation, AI, and modern web architecture, and you have learned that the hardest part is not making it work once but making it scale, stay secure, and actually get adopted by engineers who have real deadlines. You think in systems, not features. You know the difference between a prototype that impresses in a demo and a production platform that runs reliably at 3 a.m. when someone in Germany needs results, and you care deeply about that difference. You are comfortable sitting with a customer team trying to automate simulation workflows and walking out with an architecture that integrates PyAnsys services, containerized deployments, and MLOps pipelines without creating a maintenance nightmare. You do not wait for perfect specs. You ask the right questions, align stakeholders, and build something that works today and evolves tomorrow. You have a point of view on what good platform engineering looks like. You push back when an architecture is too fragile or a deployment strategy ignores observability. You are technical enough to debug a Kubernetes networking issue and strategic enough to influence product roadmaps. At Ansys, you will work on platforms that help engineering teams solve problems that matter, and the solutions you build will shape how simulation and AI come together in production. What You'll Be Doing Architect and lead development of scalable, cloud-native application platforms that integrate Ansys simulation technologies with modern web, API, and AI capabilities, owning decisions across frontend, backend, data layers, and infrastructureDesign and implement end-to-end DevOps frameworks using infrastructure as code, CI/CD pipelines, Docker, and Kubernetes to enable automated, repeatable, and reliable deploymentsDevelop and integrate Python-based servicesand PyAnsys into distributed, service-oriented architectures that enable advanced automation and simulation-driven workflowsLead MLOps adoption by building model packaging, deployment, monitoring, and lifecycle management workflows that bring AI into engineering tools without breaking productionEstablish observability best practices including logging, monitoring, alerting, and performance tuning across distributed systems and ML servicesPartner with customers, internal engineering teams, and product development to define solution architectures,validatenew capabilities, and influence long-term technology roadmapsMentor engineers on full stack development, DevOps,MLOps, and architectural best practices while leading strategic initiatives and reusable platform frameworksThe Impact You Will Have Enable engineering teams to run simulation workflows at scale with platforms that are secure, maintainable, and built for production, not just proof of conceptAccelerate customer innovation by integrating AI and ML capabilities directly into engineering workflows, reducing manualeffortand increasing delivery velocityShape how Ansys simulation technologies are deployed and consumed in cloud-native, containerized environments across diverse customer use casesReduce deployment friction and operational risk byestablishingautomated CI/CD pipelines and infrastructure as code practices that teams can rely onInfluence product strategy by translating real customer challenges into technical requirements that drive platform evolution and new capabilitiesBuild reusable frameworks and architectural patterns that raise the bar for how application platforms are designed and delivered across the organizationCreate a culture of technical excellence by mentoring engineers, sharing knowledge through workshops and content, anddemonstratingwhat good looks like in practiceWhat You'll Need Bachelor's degree, Master's, or PhD in Mechanical, Chemical, Aerospace, or Electrical Engineering, Computer Science, Mathematics, or a related fieldRequired minimum years of professional experience in an engineering software environment: BS+12, MS+10, or PhD+7Proventrack recorddesigning and leading development of scalable, production-grade web applications across frontend, backend, and API layersStrong hands-onexpertisein cloud-native architectures including Docker, Kubernetes, and CI/CD pipelines, withdemonstratedimplementation of DevOps practices like infrastructure as code and automated deploymentsStrong proficiency in Python for building services, automation scripts, and integrating with scientific or engineering libraries is a strong plusPractical experience withMLOpsworkflows including model We Are