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Strong PwC brand and Bangalore location balanced by niche LLM specialization and seniority, moderating competition.
Role requires deep LLM, GPU, and MLOps expertise, limiting cross-industry transferability.
Explicit 8-16 years requirement and many mandatory LLM, GPU, and cloud/DevOps skills enforce strict filtering.
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Design and manage ML pipelines including experiment, model, feature management, and model retraining with scalable API deployment.
Lead architecture and optimization of large language models (LLMs), including distributed training and inference using frameworks like DeepSpeed and vLLM.
Implement DevOps and LLMOps practices involving Kubernetes, Docker, and orchestration frameworks such as Flowise, Langflow, and Langgraph to support AI workloads.
8-16 years of work experience in relevant AI/ML architecture roles.
Bachelor’s degree in Engineering or Technology (B.Tech, BE) or relevant master’s degrees (MCA, M.Tech).
Proven expertise with ML pipeline tools (MLflow, SageMaker, Vertex AI, Azure AI) and LLM frameworks and GPU architecture.
Proficiency in Python, SQL, JavaScript; strong knowledge in Kubernetes, Docker, and cloud platforms (AWS/Azure/GCP).
Senior professional with deep specialization in AI architecture and LLM operationalization in cloud-native environments.
Experienced in designing scalable ML pipelines with integrated model lifecycle management and API deployment.
Strong strategic capability to optimize GPU-based LLM training and fine-tuning for latency and resource efficiency