Sr. Staff Engineer, GenAI Platform

at  Synopsys

ABOUT SYNOPSYS:

Synopsys is a global leader in electronic design automation and semiconductor IP, and we are dedicated to helping our clients innovate the electronics that are embedded in our daily lives. We are seeking a talented and driven Platform Engineer to join our team in North America to enhance our R&D efforts by leveraging modern architectures, tools, and technologies.

JOB DESCRIPTION:

As a Sr. Staff Engineer in Synopsys platform engineering team, you will play a key role in transforming our R&D products through the adoption of advanced cloud, containerization, Microservices, modern software delivery and AI technologies. Your mission will be to democratize these technologies to streamline our EDA product development processes, ensuring our products remain at the forefront of the industry.

QUALIFICATIONS:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• A minimum of 5 years of experience in software development
• Strong Development Experience in Go (highly preferred) and Python
• Deep understanding of distributed computing, CAP theorem, and system design tradeoffs.
• Proven expertise in cloud computing services (AWS, Azure, GCP), container technologies (Docker, Kubernetes)
• Background working with CI/CD pipelines and familiarity with modern software delivery ecosystem (GitHub Actions, Azure DevOps, Harness.io, GitLab CI, etc.).
• Experience with Gen AI, AI/machine learning frameworks and tools is highly desirable.
• Excellent problem-solving, organizational, and leadership skills.
• Strong communication abilities to effectively collaborate with internal teams and stakeholders.
• Linux Foundation certifications: CKAD, CKS, and/or CKA (highly preferred)

• Design and develop scalable, modular platform solutions that promote reuse and are easily integrated into our diverse product suite.
• Collaborate with cross-functional teams to understand their needs and incorporate user feedback into the platform’s development.
• Establish best practices for modern software architecture, including microservices, serverless computing, and API-first strategies.
• Drive the strategy for containerization and orchestration using Docker, Kubernetes, or equivalent technologies.
• Explore and incorporate AI and machine learning capabilities into the platform to facilitate smarter workflows and predictive analytics.
• Ensure the platform’s infrastructure is robust, secure, and compliant with industry standards.