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Mid-level, popular ML/LLM role in Bangalore with hybrid work and strong brand increases candidate competition.
Core LLM and production ML engineering skills are readily transferable across industries despite insurance domain preference.
Multiple mandatory technical filters (LLM experience, PyTorch/deepspeed, production Kubernetes, distributed architectures) raise strictness.
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Design, build, and serve Large Language Models (LLMs) on Azure (CPU & GPU) to solve complex P&C insurance business challenges.
Research and implement state of the art LLM techniques including pre-training, fine-tuning, preference alignment, and generative AI methods.
Develop novel datasets and scalable data pipelines, ensure high quality code, and build reusable processes and tools for LLM and generative AI workflows.
3+ years experience in AI/ML with deep expertise in production Python coding.
Experience in LLM engineering including pretraining and post-training/alignment techniques.
Proficiency with LLM frameworks (transformers, trl, deepspeed, PyTorch) and distributed, high throughput architectures.
Work Experience Required: 3+ years in AI/ML. Notice period: Not explicitly mentioned.
Experienced in end-to-end LLM lifecycle including dataset creation for pre-training, instruction tuning, and deployment.
Strong multidisciplinary problem solver with ability to communicate technical solutions effectively to business stakeholders.
Familiarity with container technologies (Kubernetes, Docker) and system architecture design for scalable AI solutions.