Senior Engineer

2 days ago

Pune, India Attentions AI Full time
Greetings From Attentions.ai
Job Description for Senior Engineer - PyTorch to TensorRT Conversion & CUDA Expert.
Title: Senior Engineer - PyTorch to TensorRT Conversion & CUDA Expert
Location : Pune
Job Type: Permanent
About Us:
At Attentions.ai, we're at the forefront of digital interaction and cognitive service development. We are committed to innovation and excellence, providing a dynamic work environment where creativity thrives. Join us to push the boundaries of UI technology.
Role Summary:
This role is ideal for someone passionate about pushing the limits of AI model performance and working with state-of-the-art GPU technology. Join us in revolutionizing how AI models are deployed in production --- This job description can be tailored further based on your company's specific requirements.
Job Description:
We are seeking a highly skilled Senior Engineer with deep expertise in converting PyTorch models into optimized TensorRT models and extensive experience in NVIDIA CUDA development. You will be responsible for building high-performance inference pipelines, focusing on maximising speed and efficiency through CUDA graphs and TensorRT-compiled models.
This role is crucial for accelerating our AI models in production environments and ensuring the scalability of inference workloads.
Key Responsibilities:
PyTorch to TensorRT Conversion: - Convert and optimize PyTorch models into TensorRT to achieve low-latency, high-throughput performance. Troubleshoot conversion issues and ensure compatibility between model frameworks and inference environments.
NVIDIA CUDA Optimization:
Develop high-performance inference pipelines leveraging CUDA for efficient GPU utilization. Write and optimize CUDA kernels to accelerate specific model operations when needed. Design and implement CUDA graphs to maximize inference speed.
Inference Pipeline Development:
Build scalable and high-speed model inference pipelines integrating TensorRT-compiled models.
Profile and optimize inference workloads, ensuring minimal latency and maximum throughput. Work closely with data scientists and machine learning engineers to productionize models.
Performance Tuning: Optimize memory usage, resource management, and load balancing for multi-GPU systems.
Use advanced techniques for model quantization, pruning, and graph optimizations to reduce model size and improve performance.
Collaboration:
Work with the ML research and deployment teams to integrate optimized models into production systems.
Collaborate on architecture decisions and deployment strategies for large-scale AI inference systems.
Technical Expertise:
Strong hands-on experience with PyTorch, including model training and optimization.
In-depth knowledge of TensorRT and experience in converting models from PyTorch or other frameworks (e.g., TensorFlow) into TensorRT.
Advanced proficiency in NVIDIA CUDA programming and development.
Experience with CUDA Graphs and optimizing models for low-latency inference.
Experience: Minimum of 5 years of hands-on experience in deep learning model optimization, focusing on GPU-based model acceleration.
Proven experience in building real-time inference pipelines using TensorRT and CUDA. Familiarity with NVIDIA profiling tools such as Nsight, and experience with model debugging and optimization.
Solid understanding of multi - GPU architectures and distributed computing frameworks.
Programming Skills:
Proficiency in C++ and Python, with strong debugging and problem-solving skills. Familiarity with libraries like cuDNN, cuBLAS, and other CUDA ecosystem components.
Performance Optimization: Expertise in profiling, benchmarking, and improving model inference latency and throughput.
Experience with techniques like model quantization, pruning, and compression to reduce computational load.
Preferred Qualifications:
Experience working with cloud-based AI inference systems (AWS, GCP, Azure).
Knowledge of containerization technologies like Docker, and orchestration tools like Kubernetes.
Understanding of other machine learning frameworks (TensorFlow, ONNX).
Contributions to open-source deep learning or GPU-acceleration projects.
What We Offer: Competitive salary and performance bonuses.
Work with cutting-edge AI and GPU technologies.
Collaborative and innovative work environment.
Opportunities for career advancement and skill development.
Flexible work hours and a comprehensive benefits package.
Application Process:
Please submit your resume, along with a cover letter detailing your relevant experience in PyTorch to TensorRT conversion, CUDA optimization, and high-performance inference pipeline development. Links to projects, GitHub repositories, or published papers are a plus.
Company Culture and Values:
Creativity, innovation, and a pursuit of excellence define our ethos. We are dedicated to fostering an environment where professionals can grow and contribute to cutting-edge technology. At Attentions, we value teamwork, continuous learning, and the drive to develop impactful solutions.

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