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Strong PwC brand, Bangalore location, and mid-level experience increase applicant competition.
Requires specialized generative AI and LLMOps expertise, limiting cross-industry transferability.
Specific LLM/LLMOps, GPU, Kubernetes and deep learning toolset make filters highly restrictive.
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Design and manage ML pipelines for experiment, model, and feature management plus model retraining with focus on scalable API inferencing.
Lead large language model (LLM) training, serving, fine-tuning, and optimization leveraging GPU architectures and frameworks like DeepSpeed and vLLM.
Implement and operate DevOps and LLM Ops practices including Kubernetes, Docker, and orchestration with tools like MLflow, Langflow, and Flowise.
3+ years of professional experience implementing Gen AI and LLM solutions.
Technical skills: Python, PyTorch/TensorFlow/Keras, Huggingface, Langchain, Langgraph, Docker, Kubernetes (mandatory).
Education: BE/B.Tech or equivalent in Engineering; MBA or MCA also accepted.
Experience with cloud platforms (AWS, Azure, GCP) and ML platforms such as SageMaker, Vertex AI preferred but not mandatory.
Experienced in end-to-end design and operationalization of ML pipelines with measurable impact on model performance and scalability.
Strong practical knowledge of distributed training, GPU architecture, and LLM orchestration frameworks for large-scale AI projects.
Comfortable working in cloud-native environments integrating DevOps practices with data science workflows.