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APPIT Software
4 weeks ago
Responsibilities :
- Implement cutting-edge model optimization techniques, such as quantization, pruning, and knowledge distillation, to significantly enhance model performance and reduce operational costs.
- Possess extensive experience in improving the training and inference efficiency of AI models, ensuring faster training times and lower resource consumption.
- Develop and maintain internal tools to automate and support model training, testing, and deployment processes at scale.
- Collaborate effectively with ML Engineers to integrate optimized models seamlessly into production workflows for diverse client applications.
- Conduct thorough cost-benefit analyses to identify opportunities for streamlining AI workflows and maximizing efficiency.
Technical Skills :
- AI/ML Tools : Expertise in TensorFlow, PyTorch, and ONNX for building, optimizing, and deploying machine learning models.
- Optimization Techniques : In-depth knowledge of Large Language Model (LLM) optimization techniques, including quantization, knowledge distillation, pruning, and mixed-precision training.
- GPU Optimization : Proven experience in optimizing AI models for efficient execution on GPU hardware, particularly NVIDIA and AMD architectures.
- Benchmarking Expertise : Familiarity with benchmarking tools like MLPerf to ensure optimal model performance and deployment in production environments.
- Deployment Pipelines : Proficiency in deploying models in scalable cloud environments using platforms like AWS, Azure, or GCP.
Qualifications :
- 8+ years of experience in the field of Artificial Intelligence and Machine Learning, with a specialization in model optimization and performance tuning.
- Proven track record of developing and implementing scalable AI solutions for real-world enterprise applications.
- Strong problem-solving skills with the ability to identify and address challenges related to AI model development and system integration.
- Excellent communication, collaboration, and interpersonal skills.
- A passion for innovation and continuous learning in the rapidly evolving field of AI