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Mid-level Bangalore LLM specialist at PwC with niche skills reduces but brand and metro increase competition.
Specialized GenAI and LLM skills are transferable across industries but require strong ML/LLM background.
Many mandatory niche ML/LLM skills plus explicit 3+ years requirement create strict shortlisting.
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Design and manage ML pipelines including experiment, model, and feature management with tools like MLflow, SageMaker, Vertex AI, Azure AI.
Develop and optimize GPU-based distributed training and serving of large language models using DeepSpeed, vLLM, and fine-tuning techniques to improve latency, accuracy, and reduce resources.
Implement DevOps and LLMOps practices including Kubernetes, Docker, and orchestration frameworks such as Flowise, Langflow, and Langgraph for scalable AI model deployment.
3+ years of relevant work experience in Generative AI and Large Language Models.
Bachelor's degree in Engineering (BE/B.Tech) or equivalent; Master’s preferred.
Mandatory skills: Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Not explicitly mentioned: notice period, location constraint, visa sponsorship details, or government clearance requirements.
Experienced in AI/ML engineering roles demanding hands-on design and management of LLM pipelines and infrastructure at scale.
Strong expertise in GPU architectures, distributed training, and LLM fine-tuning indicating seniority in ML operations and model optimization.
Comfortable working with cloud platforms (AWS, Azure, GCP) and DevOps tools suitable for deploying and scaling AI applications in advisory or consulting settings.