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PwC brand, mid-level experience band, and Bangalore metro raise applicant competition density.
LLM, GPU and MLOps specialization creates strong domain bias limiting cross-industry interchangeability.
Explicit 5-8 years and mandatory advanced LLM, GPU, and MLOps skills create strict screening filters.
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Design and manage ML pipelines including experiment, model, and feature management, and model inferencing at scale.
Expertise in distributed training and serving of large language models using GPU architectures and frameworks like DeepSpeed, vLLM.
Implement model fine-tuning and optimization to improve latency, accuracy, and resource efficiency for LLM and LVM models; handle DevOps and LLMOps using Kubernetes, Docker, and orchestration frameworks.
5-8 years of work experience in relevant AI/ML roles.
Mandatory skills include Gen AI, large language models (LLM), Hugging Face, and Python programming.
Required educational qualifications: B.Tech, MCA, BCA, or M.Tech degree.
Proficiency with JSON is mandatory; knowledge of MLflow, SageMaker, Vertex AI, Azure AI is also essential.
Experienced in building scalable ML pipeline architectures and managing large NLP/LLM deployments in cloud environments.
Strong background in GPU-based distributed training and LLM serving optimizations.
Familiar with DevOps and container orchestration technologies relevant to ML/LLM workflows (e.g., Kubernetes, Docker).