About the client:-
we are building cutting-edge AI tools for chip designers to streamline workflows, boost productivity, and accelerate innovation. Founded by successful entrepreneurs and second-time founders with 75+ patents to their name, we bring deep expertise in semiconductor design, AI, and automation to tackle the industry's toughest challenges. Backed by marquee investors, we are well-positioned to drive the next wave of innovation in semiconductor design. Our mission is to empower designers with intelligent assistants that act as both productivity enhancers and innovation catalysts, ensuring faster design cycles, smarter decisions, and higher efficiency in the ever-evolving semiconductor industry. We are assembling a world-class AI and full-stack development team based in Bangalore, India. If you're passionate about AI, full-stack development, and keen to work on game-changing technology, we want to hear from you!
Position Overview
Full Stack Architect to lead the end-to-end integration of UI/UX, backend, and AI components for our AI-driven EDA product. This role demands a technical leader who can design and oversee scalable, secure, and high-performance full-stack solutions.
Required Skills & Qualification
• Frontend: React.js / Angular / Vue.js, WebSockets, RESTful APIs.
• Backend: GraphQL, Node.js / Python (FastAPI, Flask, Django) / Java / Spring Boot.
• AI Integration: Experience integrating AI/ML models via REST/ GraphQL APIs / TensorFlow Serving / ONNX, or custom AI pipelines.
• Databases: Neo4j (GraphDB), PostgreSQL / MongoDB / Redis / Firebase.
• Cloud & DevOps: AWS / GCP / Azure, Kubernetes, Docker, Terraform, CI/CD (Jenkins, GitHub Actions).
• Security: OAuth2, JWT, Role-Based Access Control (RBAC), Secure API design.
• Experience with EDA tools, AI-driven design automation and real-time interface systems is a plus.
• Master’s or Bachelor’s degree in Computer Science, Engineering, or related field.
Key Responsibilities
• Define and implement a scalable and modular full-stack architecture integrating UI/UX, backend, and AI engine.
• Optimize backend services for low-latency AI inference and large dataset processing.
• Define security standards, authentication, and authorization protocols across all layers.
• Integration of AI models into the backend via APIs, microservices, or containerized deployments.
• Ensure frontend frameworks (React, Angular, Vue) interact seamlessly with backend services. • Implement best DevOps practices for CI/CD pipelines, automated testing, and deployment.
• Conduct performance benchmarking, ensure the system supports real-time and batch processing efficiently.
• Optimize backend databases (PostgreSQL, NoSQL, GraphDB) for fast AI inference and user interactions.