Lead Architect - AI Platforms & Distributed Systems

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.

• 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.

• 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.

• 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)