Lytup Power Systems Inc is building the most efficient, safe and sustainable residential we battery energy storage solutions based on lithium-ion , and battery thermal management system (BTMS) technologies. Our vision is to electrify Sub-Saharan Africa and eventually other regions of the world struggling to access power by providing small and medium battery systems coupled with PV technology that possess a lifespan of up to fifteen (15) years. Our immediate goal is to provide Africa, starting in Nigeria with smart battery energy solutions that can deliver reliable 24-hour power.
Position Overview:
- We are seeking a talented and experienced AI Engineer specializing in sound analysis and pattern recognition. In this role, you will develop algorithms and AI models that analyze audio inputs in real-time, filter background noise, and identify unique sound patterns. You’ll work closely with our cross-functional engineering team to design and implement efficient, high-performance solutions for an embedded system.
Key Responsibilities:
- Develop and optimize algorithms for sound recognition and real-time audio processing.
- Design and train AI models for pattern recognition in audio signals, focusing on precision and low-latency performance.
- Implement noise filtering and audio pre-processing techniques to enhance the accuracy of sound identification.
- Collaborate with firmware engineers to integrate AI models into an embedded system for efficient, low-power operation.
- Conduct testing and validation to ensure accuracy and reliability in varying environmental conditions.
- Continuously improve AI models by refining datasets and enhancing detection accuracy.
- Provide technical guidance on AI and machine learning best practices within the team.
Required Skills and Experience:
- Experience in AI and Machine Learning: Strong background in developing AI models, particularly for audio/sound analysis and pattern recognition.
- Audio Signal Processing: Proficiency in digital signal processing (DSP), including noise reduction, feature extraction, and frequency analysis.
- Embedded Systems: Familiarity with deploying AI models on embedded systems with a focus on optimizing for limited resources and low power consumption.
- Programming Languages: Proficient in Python and C/C++ for model development and deployment; experience with TensorFlow Lite, PyTorch Mobile, or similar tools for lightweight AI.
- Algorithm Development: Ability to design and implement custom algorithms for identifying specific patterns in audio data.
- Machine Learning Frameworks: Experience with ML frameworks like TensorFlow, Keras, or PyTorch, especially for audio classification tasks.
- Data Management: Skills in creating and managing large, labeled audio datasets for training and testing models.
- Problem-Solving Mindset: Strong analytical and critical thinking skills, with the ability to translate abstract requirements into practical, efficient AI solutions.
Preferred Qualifications:
- Experience in bioacoustics or related fields focusing on natural sound recognition.
- Knowledge of low-power hardware platforms (e.g., ARM Cortex-M, DSP chips) and microcontroller-based AI deployment.
- Familiarity with FFT (Fast Fourier Transform) and other spectral analysis techniques.
- Experience with real-time processing in challenging environmental conditions.
Method of Application
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