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Tier-1 brand and Bangalore location increase applicant density despite niche LLM seniority.
Specialized LLM/ML infra skills transferable, but advisory consulting experience increases domain specificity.
Explicit 11-15 years plus mandatory GenAI/LLM and ML infra stack make filters strict.
Design and manage RAG/LLM pipelines including experiment, model, and feature management plus model retraining.
Develop and scale APIs for model inferencing leveraging MLflow, SageMaker, Vertex AI, and Azure AI.
Lead LLM serving and GPU architecture, focusing on deep knowledge of GPU architectures, distributed training, fine-tuning, optimization, and LLMOps with Kubernetes and container orchestration.
11-15 years of relevant work experience.
Bachelor's degree in Technology or MBA; M.Tech or MCA also acceptable.
Strong mandatory skills: Gen AI, LLM, Hugging Face Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Experience with MLflow, SageMaker, Vertex AI, Azure AI, and DevOps tools; notice period and location specifics not mentioned explicitly.
Senior level professional with deep expertise in AI architecture, particularly in large language models and generative AI.
Experienced in designing and optimizing model pipelines and inferencing at scale within cloud environments (AWS, Azure, GCP).
Hands-on with DevOps and LLM orchestration frameworks, comfortable managing containerized ML workloads and fine-tuning performance of large models.