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PwC brand and Bangalore location increase applicant density despite niche LLM/ML specialization.
Highly domain-specific LLM, distributed training, and MLOps expertise limits cross-industry transferability.
Explicit 8-16 years and many mandatory LLM, GPU, cloud, and DevOps skills enforce strict shortlisting.
Design and manage machine learning pipelines including experiment, model, feature management and retraining using MLflow, SageMaker, Vertex AI, Azure AI.
Architect and optimize large language model (LLM) serving with expertise in GPU architectures, distributed training frameworks (DeepSpeed, vLLM) and fine-tuning for performance and resource efficiency.
Implement DevOps and LLMOps practices using Kubernetes, Docker, and LLM orchestration frameworks (Flowise, Langflow, Langgraph) to deploy AI models at scale.
8-16 years of relevant industry experience in AI, machine learning pipeline design, and LLM model management.
Mandatory proficiency with MLflow, SageMaker, Vertex AI, Azure AI, Python, SQL, JavaScript, Kubernetes, and Docker.
Bachelor or Master degree in Engineering or Computer Science (B.Tech/MCA/BCA/M.Tech) is required.
Not explicitly mentioned: Notice period, specific location constraints, or visa sponsorship conditions.
Experienced in designing end-to-end ML pipelines and model inferencing APIs at enterprise scale with cloud AI services (AWS, Azure, GCP).
Strong background in distributed training and fine-tuning of large language models optimizing for latency and resource use.
Capable of integrating DevOps and LLMOps tooling and frameworks to streamline deployment and monitoring of AI workloads in production environments.