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Metro Bangalore, mid-level 3–5 year ML role with moderate specialization yields medium competition.
Core ML/LLM skills transfer across industries, fintech focus adds moderate sensitivity.
Explicit 3–5 years plus mandatory LLM post-training and specialized ML stack makes shortlisting high.
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Lead model selection strategy and build evaluation frameworks for production AI systems focusing on LLM quality and performance.
Architect and implement the core retrieval and memory layers including scalable retrieval pipelines and ranking systems for AI.
Drive model improvement through post-training techniques such as fine-tuning and reinforcement learning, supported by research-based experimentation.
3–5 years of experience in Machine Learning or Applied AI.
Strong hands-on experience with XGBoost, NLP, recommendation systems, personalization, and large-scale retrieval.
Mandatory expertise in Large Language Model (LLM) architecture, including training, inference, and post-training methods like fine-tuning or reinforcement learning.
Location requirement: Bengaluru.
Demonstrated ability to independently formulate and solve ambiguous ML problems from first principles.
Experience in designing robust model evaluation and benchmarking frameworks specifically for LLMs.
Comfortable operating in a research-driven, applied AI fintech environment with cross-functional collaboration.