Principal Engineer

6 days ago

Mumbai, India Success Pact Consulting Pvt Ltd Full time

Responsibilities :


- You will provide leadership in designing and implementing groundbreaking GPU computers that run demanding deep learning, high-performance computing, and computationally intensive workloads.

- We seek an expert to identify architectural changes and/or completely new approaches for accelerating our deep learning models.

- As an expert, you will help us with the strategic challenges we encounter, including compute, networking, and storage design for large-scale, high-performance workloads, effective resource utilization in a heterogeneous computing environment, evolving our private/public cloud strategy, capacity modelling, and growth planning across our products and services.

- As an architect, you are responsible for converting business needs associated with AI-ML algorithms into a set of product goals covering workload scenarios, end-user expectations, compute infrastructure, and time of execution; this should lead to a plan for making the algorithms production-ready.

- Benchmark and optimise the Computer Vision Algorithms and the Hardware Accelerators for performance and quality KPIs.

- Optimize algorithms for optimal performance on the GPU tensor cores.

- Collaborate with various teams to drive an end-to-end workflow from data curation and training to performance optimization and deployment.

- Provide technical leadership and expertise for project deliverables.

- Leading, mentoring, and managing the technical :


- MS or PhD in Computer Science, Electrical Engineering, or related field.

- A strong background in the deployment of complex deep learning architectures.

- 5+ years of relevant experience in at least a few of the following relevant areas is required in your work history: Machine learning (with focus on Deep Neural Networks), including an understanding of DL fundamentals.

- Experience adapting and training DNNs for various tasks.

- Experience developing code for one or more of the DNN training frameworks (such as Caffe, TensorFlow, or Torch): Numerical analysis, Performance analysis, Model compression and Optimization, and Computer architecture.


- Strong Data structures and Algorithms know-how with excellent C/C++ programming skills.

- Hands-on expertise with PyTorch, TensorRT, and CuDNN.

- Hands-on expertise with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP).

- In-depth understanding of container technologies like Docker, Singularity, Shifter, and Charliecloud.

- Proficient in Python programming and bash scripting.

- Proficient in Windows, Ubuntu, and CentOS operating systems.

- Excellent communication and collaboration skills.

- Self-motivated and able to find creative, practical solutions to problems.


(ref:hirist.tech)

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