AI/ ML Software Engineer
Salary Range:
Job Summary
AI/ML Developer
AI/ML model performance optimizer
EDA Tools API Integration
Desktop Application Developer
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Key Responsibilities:
1. Integrate with Electronic Design Automation (EDA) tools' APIs to access and analyze electronic circuit data.
2. Develop frontend and backend components for a desktop application using C# and Python.
3. Experience with using EDA software for design and automation.
4. Very experience in image-processing, computer vision, and fast & accurate object detections.
5. Implements image processing & classification using AI/ML techniques such as Convolutional Neural Network (CNN) or transformer-based architecture .
6. Analyze and enhance spectrogram conversion and clarity by using various Digital Signal Processing (DSP) techniques into signal processing pipeline before image classification.
7. Collect, analyze and process large datasets with various data processing and manipulation skills to create more reliable datasets for most accurate model training in various AI/ML fields & applications.
8. Collaborate with engineers and designers to create user-friendly interfaces and ensure seamless user experiences.
9. Troubleshoot and optimize software performance, addressing issues related to data processing and visualization.
Qualifications:
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
Experience:
Experience with using EDA software for design and automation.
Very experience in image-processing, computer vision, and fast & accurate object detections.
Skillsets:
Very proficient in C++, C# and Python for desktop application development.
Constantly keeping up to the latest AI/ML technology and trends and their application in the electronic design domain.
Very strong & in-depth problem-solving and debugging skills.
Ability to work independently and collaboratively in a dynamic environment.
Excellent communication and interpersonal skills.
Having Radio Frequency (RF) knowledge is an added advantage.
Python, C++ programming
AI/ML techniques (Reinforcement Learning, multi-agent system, CNN)
Performance Profiling techniques (memory, computational latency)
Statistical techniques (Optimization, Normalization & Standardization, peak finding, feature extractions)
Software UI/UX
Software deployment compilation pipeline
CI/CD
Optional Skillsets: