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AI System Architect

2 weeks ago


Hyderabad, Telangana, India LeadSoc Technologies Pvt Ltd Full time

Job Overview:

We are seeking a highly skilled AI System Architect with expertise in designing, training, and fine-tuning large models and deep neural networks. The ideal candidate should have a deep understanding of AI model development, deployment, and optimization, along with experience in embedded systems.

About Us:

LEADSOC Technologies is a leading provider of product engineering and design services focusing on VLSI and embedded systems. We offer a wide range of solutions to clients across various industries, including automotive systems, consumer electronics, telecommunications systems, semiconductors market, and healthcare industry segments.

Key Responsibilities:

Design, train, and fine-tune large models and deep neural networks for various AI applications.Develop and implement LLM architectures to solve complex problems in NLP, CV, and other AI domains.Optimize AI models for performance, scalability, and efficiency across diverse computing environments.Research and apply the latest advancements in deep learning, reinforcement learning, and AI model compression techniques.Architect end-to-end AI systems, ensuring seamless integration of hardware and software components.Design AI pipelines for training and continuous model improvement.Optimize data pipelines for high-throughput AI workloads.Collaborate with DevOps and MLOps teams to deploy AI models efficiently on cloud and edge environments.

Requirements:

  • Hands-on experience in training and deploying large-scale AI models.
  • Proficiency in designing, training, and fine-tuning large language models (LLMs) for NLP tasks.
  • Experience in architecting AI-driven systems, including cloud and edge deployments.
  • Familiarity with Memory/Storage or any embedded platform for AI/ML applications.
  • Expertise in Python, TensorFlow, PyTorch, and other AI/ML frameworks.
  • Experience with GPUs, TPUs, FPGAs, and AI accelerators for optimizing AI workloads.
  • Familiarity with cloud platforms (AWS, Azure, GCP) and edge AI deployment strategies.
  • Understanding of data pipelines, model deployment, and AI lifecycle management.