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PwC brand and Bangalore metro raise competition, while senior niche LLM specialization moderates candidate density.
Role requires deep LLM, GPU, and MLOps expertise, making cross-industry transferability limited.
Explicit 8–16 years requirement and many mandatory LLM, GPU, cloud, and DevOps skills enforce strict shortlisting.
Design and implement ML pipelines for experiment management, model management, feature management, and model retraining, including scalable API design for model inferencing.
Lead distributed training and serving of large language models leveraging GPU architectures, optimizing model fine-tuning for latency, accuracy, and resource efficiency.
Apply DevOps and LLMOps expertise using Kubernetes, Docker, and LLM orchestration frameworks to maintain and optimize AI/ML deployments.
8-16 years of relevant experience in ML pipeline design, LLM serving, GPU architecture, and DevOps for AI/ML systems.
Education: Bachelor’s or Master’s degree in Engineering (B.Tech/M.Tech) or MCA/BCA.
Proven expertise with MLflow, SageMaker, Vertex AI, Azure AI, DeepSpeed, vLLM, Kubernetes, Docker, and LLM orchestration tools (Flowise, Langflow, Langgraph).
Proficiency in Python, SQL, and Javascript as mandatory programming languages.
Experienced AI/ML architect with strong operational focus on scalable LLM model deployment and optimization in cloud environments (AWS, Azure, GCP).
Skilled in combining data engineering, model fine-tuning, and DevOps to improve performance metrics and reduce computational costs.
Comfortable working with advanced ML lifecycle tools and distributed training frameworks, suitable for senior associate/advisory roles.