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Metro mid-level ML role balanced by niche LLM/retrieval requirements, giving moderate competition.
Highly specialized LLM, retrieval and RL fine-tuning requirements limit cross-industry transferability.
Explicit 3–5 years plus mandatory LLM post-training, retrieval and evaluation expertise enforces strict filtering.
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Lead model selection strategy and evaluation framework development for production AI systems.
Architect and develop the retrieval and memory layer, including scalable retrieval pipelines and ranking systems.
Apply post-training techniques like fine-tuning and reinforcement learning to improve model quality, driving continuous improvement through research-backed 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 experience with LLM post-training methods (fine-tuning and/or reinforcement learning).
Location: Bengaluru.
Experienced in first-principles problem solving and defining ambiguous ML problems independently.
Demonstrated ability to design model evaluation frameworks and benchmarking methodologies.
Proven track record working with large language model architectures, retrieval systems, embeddings, and memory architectures in production environments.