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Well-funded brand, mid-level popular ML role, and Bangalore metro location increase applicant competition.
Skills are transferable across industries but require specialized ML/LLM expertise.
Explicit 3+ years requirement plus mandatory LLM fine-tuning and PEFT skills create strict technical filters.
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Develop and fine-tune large-scale machine learning models including LLMs and transformers to deliver domain-specific AI solutions.
Implement and optimize ML pipelines using tools such as PyTorch, TensorFlow, and Hugging Face, ensuring scalable production deployment.
Collaborate with cross-functional teams and potentially mentor junior scientists to translate research into impactful AI products.
3+ years of hands-on experience in applied machine learning and data science.
Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or related field, or equivalent experience.
Proven expertise in training and fine-tuning large-scale models (LLMs, transformers, diffusion models) including Parameter-Efficient Fine-Tuning methods (LoRA, prefix tuning, adapters, quantization-aware training).
Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face) and basic knowledge of MLOps practices (model versioning, CI/CD, production deployment).
Demonstrated ability to adapt foundation models through transfer learning or fine-tuning for domain-specific applications with strong validation and bias/fairness evaluation.
Experience working on applied AI problems in NLP, computer vision, multimodal systems, or related domains, showing robust problem-solving skills.
Track record of collaborating cross-functionally and leading or mentoring technical teams to deliver scalable AI solutions aligned with business goals.