VLSI Engineer

Responsibilities:

Design, develop, troubleshoot, or debug software tools for the development of integrated circuits
Support a global design team
Interface with foundries, configure the IP Project environment and design flows for IOLIBS & ESD.
Develop routines and utility programs to automate the design environment
Build productive internal/external working relationships
Requirements
Degree in electronic/micro electronic engineering or computer science
4+ years of relevant experience
Good knowledge of hardware integrated circuits and UNIX/Linux user knowledge
Proficiency with at least one programming language and scripting languages to automate processes
Good English communication skills
Good analysis and problem-solving skills and the ability to exercise good judgment
Experience in Python, TCL or Shell scripting
Experience with VLSI domain like familiarity with DRC/LVS Extraction/Simulation/EMIR
Cadence, Custom Compiler EDA tool usage
IC design experience
Experience in data management and job schedule tool like LSF/GRID Engine
Benefits
Azure Infrastructure Experience: Proficiency in managing Azure infrastructure components, including virtual machines, storage, and networking, to support AI model development and deployment.
CI/CD Pipeline Experience: Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines, including the automation of model deployment processes.
Containerization in the Cloud: Strong knowledge of containerization technologies in the cloud, such as Docker and Kubernetes, for efficient deployment and scaling of machine learning models.
Machine Learning Expertise: Proficient in building and optimizing machine learning models, with a deep understanding of various ML algorithms and frameworks.
Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python and libraries like TensorFlow and PyTorch.
Data Management: Experience in data preprocessing, feature engineering, and data pipeline development for machine learning.
Collaborative Team Player: Excellent communication skills and the ability to work collaboratively with cross-functional teams, including AI engineers and SREs.
Documentation: Effective documentation skills to maintain clear and organized records of models, infrastructure configurations, and incident responses.