Lead Engineer (HPC, GPU, CUDA)

AIRA Matrix; Thane; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

AIRA Matrix in Maharashtra seeks an AI Architect to identify architectural changes or new approaches for accelerating deep learning models and to translate business needs into concrete product goals covering workloads, user expectations, compute infrastructure and execution time, leading to a plan to productionize the algorithms.

Benchmark and optimize computer vision algorithms for performance and quality KPIs on heterogeneous hardware stacks (GPU + CPU).

Qualifications

• Bachelor's degree or higher in CS, EE, or related field.
• 1+ years of experience in machine learning with DL fundamentals and model optimisation.
• Strong C++ programming skills and data structures/algorithms expertise.
• Experience with DL frameworks (Torch, TensorFlow, Caffe) and GPU acceleration.
• Familiarity with CUDA, CuDNN, TensorRT and HPC tools.
• Proficient Python and Bash scripting; comfortable with Linux environments.
• Experience with containerization and orchestration tools (Docker, Kubernetes).

Responsibilities

• Identify architectural changes or new approaches to accelerate deep learning models.
• Translate business needs into product goals covering workloads, end user expectations, compute infrastructure and execution time; plan production readiness.
• Benchmark and optimize computer vision algorithms for performance and quality KPIs on heterogeneous hardware (GPU+CPU).
• Collaborate with teams to drive end-to-end workflow from data curation and training to deployment and performance optimization.

Skills

C++ programming

Data structures & algorithms

Python programming

CUDA / GPU computing

DL frameworks

TensorRT

Linux/Ubuntu

Docker / containers

Kubernetes / HPC schedulers

Communication skills

Education

Bachelor's degree in CS, EE, or related field

Tools

CMake

Make/Ninja

Clang-Tools

TensorRT

CuDNN

PyTorch

TensorFlow

Docker

Singularity

Shifter

Charliecloud

Kubernetes

SLURM

LSF

OpenMP

MPI

Job description

• We seek an expert to identify architectural changes and/or completely new approaches for accelerating our deep learning models.
• 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 optimize the Computer Vision Algorithms for performance and quality KPIs on the heterogeneous hardware stacks (GPU + CPU, etc.)
• Collaborate with various teams to drive an end to end workflow from data curation and training to performance optimization and deployment.

Skills Required:-

• Bachelors or Higher in Computer Science, Electrical Engineering, or related field. A strong background in deployment of complex deep learning architectures.
• 1+ 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 understanding of DL fundamentals; Experience adapting and inferencing DNNs for various tasks; Experience developing code for one or more of the DNN training frameworks (such as Torch, Caffe or TensorFlow): Numerical analysis, Performance analysis, Model compression and Optimization & Computer architecture.
• Strong data structures and algorithms knowhow with excellent modern C++ programming skills.
• Good grasp over software engineering and tools like CMake, Make (or Ninja), Clang-Tools, etc.
• Hands-on expertise with TensorRT, CuDNN, PyTorch
• Hand-on expertise with GPU computing (CUDA, OpenCL or OpenACC) and HPC (MPI, OpenMP)
• 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.

Good to have:-

• Hands-on experience with PTX-ISA for CUDA or vector intrinsics like AVX, SSE, etc.
• In-depth understanding of container technologies like Docker, Singularity, Shifter, Charliecloud.
• Hands-on experience with HPC cluster job schedulers such as Kubernetes, SLURM, LSF.
• Familiarity with cloud computing architectures
• Hands-on experience with Software Defined Networking and HPC cluster networking.
• Working knowledge of cluster configuration management tools such as Ansible, Puppet, Salt.
• Understanding of fast, distributed storage systems and Linux file systems for HPC workloads.