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PwC brand, mid-level role, Bangalore location, and popular GenAI skillset increase competition.
Highly specific GenAI/LLM and ML engineering skills limit cross-industry transferability.
Multiple mandatory GenAI/LLM technologies and explicit experience requirement create strict technical filters.
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Design and manage ML pipelines including experiment, model, and feature management, plus scalable model inferencing APIs.
Operate and optimize deployment and distributed training of large language models using GPU architectures and frameworks like DeepSpeed and vLLM.
Apply DevOps and LLMOps best practices for container orchestration with Kubernetes and Docker, integrating LLM orchestration frameworks.
3+ years of work experience in generative AI and large language models (LLM) related roles.
Mandatory technical skills: Gen AI, LLMs (Hugging Face, GPT, etc.), Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Education: Bachelor of Engineering or Master of Engineering degree.
Work Experience Required: Minimum 3 years. Notice period: Not explicitly mentioned in the JD.
Experienced in handling production-grade ML pipelines and scalable AI model deployment.
Strong expertise in large language models including fine-tuning, optimization, and distributed training on GPU clusters.
Familiar with integrating DevOps practices specifically for AI/ML model operations and orchestration on cloud platforms.