Nuvia
General Summary:
We are seeking a highly skilled Lead Software Engineer to design and build large‑scale enterprise engineering solutions that power critical workflows across chip design, qualification, and debug. In this role, you will architect cloud‑native systems, develop high‑performance services, and drive engineering excellence across teams.
Minimum Qualifications:
• Bachelor's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 3+ years of Hardware Engineering or related work experience.Key Responsibilities
Architect and implement scalable, cloud‑native enterprise applications and backend services.
Lead system design efforts focusing on distributed systems, reliability, and performance.
Develop reusable frameworks, internal platforms, and automation tools for engineering teams.
Build secure, robust APIs and service layers integrating with SQL/NoSQL systems.
Partner with cross‑functional leads to define technical architecture and long‑term roadmaps.
Mentor engineers, conduct design reviews, enforce coding standards, and improve system quality.
Diagnose complex system‑level issues and drive high‑impact technical resolutions
Minimum Qualifications:
Bachelor’s degree in Computer Science
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:
Master’s degree in Computer Science or related field.
7-12 Years of Experience.
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)
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