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Tier-1 brand, metro location, popular ML title, mid-level experience create high applicant competition.
Specialized hardware deployment and LLM optimization expertise reduces transferability across industries.
Explicit 5-10 years plus mandatory LLM, DL, deployment, and hardware optimization skills increases filter strictness.
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Fine-tune and optimize large language models for production on Qualcomm platforms focusing on performance and efficiency.
Develop AI/ML solutions leveraging TensorFlow, NLP, Transformers, and deploy them across multiple OS environments (Android, Windows, Linux).
Collaborate with cross-functional teams and external developers/startups to provide technical guidance and document solutions architecture.
5 to 10 years of experience applying AI and machine learning techniques to practical solutions.
Proficiency in Python, scripting, C/C++ programming; additional Java skills preferred.
Experience with deploying and optimizing large language models (LLMs) including fine-tuning, quantization, pruning, and distillation.
Bachelor’s degree in Engineering, Computer Science, Information Systems, or related field with 3+ years Software Engineering experience, OR Master’s with 2+ years, OR PhD with 1+ year.
Deep expertise in transformer-based architectures (e.g., BERT, GPT, T5, Llama) and practical deployment experience in ML/NLP systems.
Experience working with large-scale datasets, preprocessing, and advanced model optimization techniques on heterogeneous hardware (CPU, GPU, NPU).
Effective in collaborative cross-functional environments, capable of engaging with external communities, startups, and developers for ecosystem enablement.