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Strong PwC brand, Bangalore location, and 3+ years mid-level amplify competition despite niche LLM specialization.
Specialized LLM and MLOps skills make cross-industry fit moderately transferable.
Explicit 3+ years plus many mandatory LLM, MLOps, and cloud technical requirements imply high shortlisting strictness.
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Design and manage machine learning pipelines including experiment, model, and feature management, plus model retraining and scalable inferencing APIs.
Lead deployment and orchestration of large language models (LLMs) on GPU architectures using frameworks like DeepSpeed and vLLM.
Implement model fine-tuning and optimization to improve latency, accuracy, and reduce resource consumption; apply DevOps and LLMOps practices with Kubernetes, Docker, and orchestration tools.
Minimum 3+ years work experience in generative AI, LLM development, and related technologies.
Bachelor's degree in Engineering (BE/B.Tech) or Master's degree (M.E) required; MBA or MCA also acceptable.
Mandatory technical skills: Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Not explicitly mentioned: Notice period, location restrictions, or mandatory certifications.
Experienced in deploying and managing production-scale ML pipelines for generative AI and LLM applications in cloud environments (AWS, Azure, GCP).
Strong expertise in GPU-based distributed training, fine-tuning, and optimization of LLMs with proficiency in open source and commercial ML frameworks and orchestration tools.
Skilled in integrating DevOps practices and container orchestration to maintain scalable and efficient AI model operations.