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Strong PwC brand and metro location but niche LLMOps skills reduce candidate density.
Highly specialized LLMOps, GPU/distributed training and MLOps requirements limit cross-industry transferability.
Explicit 12–16 years plus mandatory LLMOps, GPU and ML infrastructure skills enforce strict filtering.
Design and implement end-to-end ML pipelines including experiment, model, and feature management, plus scalable model inferencing APIs.
Lead model fine-tuning and optimization for large language models (LLMs), improving latency and accuracy while reducing training resources.
Manage deployment and orchestration of LLMs using advanced GPU architectures and DevOps/LLMOps tools like Kubernetes, Docker, MLflow, and LLM orchestration frameworks.
12-16 years of relevant experience in ML engineering and LLM deployment.
Bachelor’s degree in Technology (B.Tech), MCA, BCA, or M.Tech.
Proven expertise in ML pipeline design and large language model serving, including frameworks like MLflow, SageMaker, DeepSpeed, and vLLM.
Strong knowledge of DevOps tools (Kubernetes, Docker) and cloud platforms (AWS, Azure, GCP).
Experienced in designing scalable ML systems with emphasis on experiment and model lifecycle management in an enterprise advisory context.
Deep technical expertise in distributed LLM training, GPU architecture, and fine-tuning optimization techniques.
Skilled in DevOps and container orchestration with LLM-specific tooling, suited to support client advisory projects in data and AI analytics.