Artificial Intelligence Engineer

LeadSoc Technologies Pvt Ltd; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Benefits offered by this job

Edge AI deployment

Generative AI systems

Distributed inference pipelines

Job summary

LeadSoc Technologies Pvt Ltd in Bengaluru is seeking an AI Engineer to optimize and deploy ML models across heterogeneous platforms including CPU, GPU, and NPU. You will work on scalable, production-ready AI systems spanning robotics, healthcare, and automotive domains.

The role requires deep knowledge of model optimization, quantization, and performance tuning, with hands-on experience in PyTorch and CUDA. Collaboration with cross-functional teams is essential.

Qualifications

• Strong in PyTorch (or similar), ONNX (or equivalent)
• Proficient in Python and C++
• Experience with GPU/hardware acceleration (CUDA/ROCm or similar)
• Solid understanding of deep learning models (transformers, CNNs)
• Knowledge of optimization, quantization, and performance tuning

Responsibilities

• Optimize diverse models: generative (LLMs, diffusion), vision (classification, detection, segmentation), multi-modal, and speech
• Deploy on hardware accelerators (GPU/NPU) and optimize performance
• Improve inference latency, throughput, and memory (batching, caching, parallelism, fusion)
• Apply quantization and model compression (FP32 → lower precision)
• Profile and debug system and model performance

Skills

PyTorch

Python

C++

CUDA

ROCm

Transformers

CNNs

Optimization

Quantization

Performance tuning

Tools

PyTorch

ONNX

CUDA

ROCm

Job description

Job Title: AI Engineer – Model Optimization & Acceleration

Role Overview

Seeking an AI Engineer to optimize and deploy ML models across heterogeneous platforms (CPU, GPU, NPU).

Work on scalable, production-ready AI systems across domains like robotics, healthcare, and automotive.

Key Responsibilities

• Optimize diverse models: generative (LLMs, diffusion), vision (classification, detection, segmentation), multi-modal, and speech
• Deploy on hardware accelerators (GPU/NPU) and optimize performance
• Improve inference latency, throughput, and memory (batching, caching, parallelism, fusion)
• Apply quantization and model compression (FP32 → lower precision)
• Profile and debug system and model performance

Required Skills

• Strong in PyTorch (or similar), ONNX (or equivalent)
• Proficient in Python and C++
• Experience with GPU/hardware acceleration (CUDA/ROCm or similar)
• Solid understanding of deep learning models (transformers, CNNs)
• Knowledge of optimization, quantization, and performance tuning

Good to Have

• Edge AI or embedded deployment
• Generative or multi-modal AI systems
• Distributed inference or streaming pipelines