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Noida, Uttar Pradesh, India
Onsite
Position: Lead Software Engineer (Python API/agentic)
Location: Noida
Experience: 4-6 Years
Cadence Design Systems is seeking a Lead Software Engineer to help lead the GUI and core workflow architecture for Quantus Insight, our next-generation parasitic extraction platform. Built from scratch less than four years ago, this modern codebase offers a rare EDA environment: minimal legacy burden with significant opportunities for greenfield project work.
Based in the Quantus Insight R&D (Digital & Signoff Group), you will collaborate closely with leadership to design the application architecture using the Qt Framework and optimize the core structures supporting it. Because our databases frequently exceed one billion elements, you will solve complex scalability challenges by designing highly optimized, multi-threaded workflows with UI support that maintain high performance under heavy data loads.
This position also offers the opportunity to integrate Agentic AI workflows, transitioning a standard desktop GUI into an AI-assisted development environment. Backed by strong internal investment and access to advanced models, this role allows you to develop skills in LLM workflows while shaping technology used to design electronic devices worldwide.
Job Responsibilities:
1. Architecture & Design (Core Engine & Workflow)
High-Load Dataflow & Workflow Engineering: Design, organize, and standardize robust internal dataflows, defining how massive datasets smoothly migrate between the computational backend and downstream platform workflows.
Core Optimization: Architect high-performance, UI-agnostic core structures and algorithms to optimize data throughput, memory footprints, and heavy computation cycles.
Asynchronous Multi-Threading: Implement advanced multi-threading and parallel processing patterns to isolate heavy background database operations from the application's interactive layers.
2. Agentic AI Integration
Workflow Composition: Compose and orchestrate our existing system functionalities and APIs into a unified, next-generation AI-native workflow.
LLM Integration: Expose internal application states, core structures, and tool operations via clean programmatic interfaces accessible to LLMs.
Dynamic UI Syncing: Ensure that autonomous actions, modifications, and state changes executed by the AI agent are instantly reflected and dynamically updated within the Qt user interface.
End-to-End Automation: Help shape a system where AI acts as an autonomous active user—capable of running verification checks, interpreting results, and programmatically driving the software.
3. GUI Development (Qt Framework)
Widgets Implementation: Drive the hands-on development and implementation of the Quantus Insight graphical user interface using C++ and the Qt Widgets framework.
Big Data Presentation: Build and customize high-scale Qt Model/View components (trees, tables, forms) optimized for lazy loading, data paging, and minimal memory overhead.
Linux Environment Profiling: Deeply profile, debug (using GDB/Valgrind), and tune UI performance under extreme memory and CPU utilization in Linux desktop environments.
Job Qualifications & Experience
Education: Bachelor’s, Master’s, or PhD in Computer Science, Software Engineering, or a related technical field.
Experience: 4+ years of professional software development experience (or 5+ years with an advanced degree) focused on complex desktop application UIs.
Required Skills:
Expert C++: Deep proficiency in modern C++ (C++ 17 or newer) including STL
High-Performance Computing: Proven track record in multi-threading, parallel programming, and data structure optimization.
Linux Expertise: Advanced development, debugging (GDB, Valgrind), and performance profiling in Linux environments.
Software Engineering: Strong grasp of object-oriented design, design patterns, and the full Software Development Life Cycle.
Scripting: Proficient in Python for automation and tool integration.
Additional Skills/Preferences:
QT Framework: Experience with the Qt framework for GUI development is a big plus.
EDA Experience: Knowledge of parasitic extraction, layout connectivity, or physical verification.
AI/LLM Application (Experienced or Aspiring): Hands-on experience building agentic workflows or working with LLM APIs (e.g., OpenAI, Anthropic) is highly desirable; however, a strong personal interest, hobbyist experimentation, and a willingness to learn how to apply AI concepts to core software engineering workflows will work perfectly.