Lead Architect - AI Platforms & Distributed Systems

Job description

General Summary: Our team is focused on applying state-of-the-art AI and ML technologies to solve complex business problems in the chip design, qualification, and debug engineering processes. We aim to enhance engineering efficiency through intelligent automation, data-driven insights, and innovative tool development. This role focuses on building scalable cloud-native AI applications that enhance engineering productivity. You'll work on distributed systems, cloud platforms, and user-facing tools that integrate AI/ML capabilities into chip design and debug workflows. Key Responsibilities - Design and implement AI/ML applications and pipelines using LLMs, GenAI platforms, and custom models to address real-world engineering challenges. - Build intelligent agents and user interfaces that integrate seamlessly into engineering workflows. - Develop robust data ingestion, transformation, and analysis pipelines tailored to chip design and debug use cases. - Build distributed computing architectures and scalable backend services. - Design intuitive user interfaces and intelligent agents for engineering teams. - Implement robust ETL pipelines and integrate with SQL/NoSQL databases. - Collaborate with team leads to ensure timely delivery and high-quality solutions. - Provide technical support and training to internal teams, helping resolve issues and improve adoption. - Engage in cross-functional initiatives to solve technical problems across departments. - Continuously contribute to the innovation and evolution of tools, technologies, and user experiences. Minimum Qualifications: - Bachelors/Master's degree in Computer Science with minimum 7+ years of work experience - Very Strong in data structures, algorithms, and software design principles. - Strong programming skills in Python and C/C++. - Experience in building scalable software systems and solving complex technical problems. - Experience with software architecture and design patterns. - Fast learner with a strong technical foundation and ability to work independently. - Very Strong Analytical and problem Solving Skill. Preferred Qualifications: - Proficiency in data analysis using Python libraries (e.g., Pandas, NumPy, Matplotlib). - Familiarity with SQL databases, ETL frameworks, and cloud platforms (e.g., AWS). - Hands-on experience on Cloud Scale Distributed Computing Architectures. - Hands-on experience with AI/ML technologies including: - Supervised and unsupervised learning - LLMs, GenAI platforms, and RAG optimization - ML frameworks like scikit-learn, PyTorch, TensorFlow - LLM integration frameworks (e.g., LangChain)