- Role: Deep Learning System Engineer
- Exp: 8 to 15 Years
- Location: Bangalore
- Industry: Hardware/ Semiconductor

Role Overview
- We are looking for an experienced AI Engineer to optimise and run deep learning models on a specialised many-core MIMD processor. The role involves developing high-performance AI operations, improving model execution speed, and ensuring accuracy and efficiency without relying on conventional GPU tensor cores.

- Key Responsibilities
- 1. AI Workload Optimisation

- Analyse and optimise AI models, including Transformers, CNNs, GNNs, recommendation systems, and diffusion models, for execution on the MIMD accelerator.
- Develop and optimise core AI operations such as matrix multiplication, convolution, attention, normalisation, activation, and reduction.
- Implement and benchmark custom AI kernels using FP16, FP32, and BF16 precision.
- Compare performance against established CUDA and cuDNN implementations on comparable hardware.

1. Framework Integration and Performance Validation

- Integrate optimised AI operations with frameworks such as PyTorch, vLLM, and Hugging Face Transformers.
- Validate model accuracy, numerical stability, and compatibility across different precision formats.
- Measure and optimise training and inference performance, including throughput, latency, memory utilisation, and power consumption.
- Collaborate with hardware and platform teams to identify performance bottlenecks and implement improvements.

1.



Model Validation and Customer Support

- Validate and maintain a portfolio of AI models, including Llama, BERT, GPT-class models, ResNet, Stable Diffusion, and GNNs, on the company's processor.

- Develop performance benchmarks, technical documentation, and model deployment guidelines.
- Support early-access customers with model porting, performance optimisation, debugging, and troubleshooting.

- Required Qualifications
- B.Tech/M.Tech, M.S., or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field.
- 8–15 years of experience in deep learning systems engineering, AI accelerator software, or ML infrastructure.

- Hands-on experience developing and optimising custom AI kernels at the hardware or low-level software interface.
- Solid programming skills in C/C++, Python, and CUDA.
- In-depth understanding of deep learning architectures, including Transformers, CNNs, GNNs, and diffusion models.

- Experience optimising matrix multiplication, attention, convolution, and other computationally intensive AI operations.
- Strong knowledge of PyTorch internals, operator dispatch, custom backends, and mixed-precision computing.

- Understanding of processor architecture, memory hierarchy, compilers, operating systems, and hardware-software interactions.
- Experience with AI performance profiling, benchmarking, numerical stability, and quantization.

📌 Deep Learning System Engineer (Bangalore Urban)
🏢 Saaki Argus And Averil Consulting
📍 Bangalore Urban