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PwC brand and Bangalore location increase competition despite niche GenAI skill requirements.
Highly specialized GenAI, LLMOps and GPU expertise limits cross-industry transferability.
Explicit 3+ years plus many mandatory GenAI, LLMOps, GPU and cloud skills make filters strict.
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Design and manage machine learning pipelines covering experiment, model, and feature management, plus model retraining and scalable inferencing APIs.
Lead distributed training and serving of large language models (LLMs), including fine-tuning and optimization to improve latency, accuracy, and resource efficiency.
Implement DevOps practices for LLM operations using Kubernetes, Docker, and LLM orchestration frameworks like Flowise and Langflow.
At least 3+ years of work experience in relevant AI/ML roles.
Proficiency with Gen AI and Large Language Models (LLM), including Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, and Kubernetes.
Bachelor's or Master's degree in Engineering (BE/B.Tech or ME) or equivalent (MBA/MCA also mentioned).
Not explicitly mentioned: notice period or strict location/on-site requirements.
Experience with ML pipelines and model lifecycle management tools such as MLflow, SageMaker, Vertex AI, and Azure AI.
Strong knowledge of GPU architectures and distributed training frameworks like DeepSpeed; familiar with LLM operations frameworks (e.g., vLLM, Langflow).
Comfortable working in cloud environments (AWS/Azure/GCP) with database experience (e.g. DynamoDB, MongoDB, SQL variants) and DevOps tools for container orchestration.