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Tier-1 startup brand, mid-level ML role, metro Bangalore, and broad LLM skill demand increase competition.
Specialized LLM and deep learning expertise creates high domain bias and limited cross-industry transferability.
Mandatory 3+ years plus specialized LLM, PEFT, and deep learning expertise makes filtering highly strict.
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Own end-to-end adaptation of large-scale foundation models to domain-specific applications via fine-tuning or transfer learning.
Design, evaluate, and optimize machine learning models using robust validation, bias/fairness checks, and performance techniques.
Collaborate cross-functionally to deliver scalable AI solutions integrated into commerce workflows for Fortune 100 clients.
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 with large-scale models (LLMs, transformers, diffusion models) and Parameter-Efficient Fine-Tuning (LoRA, prefix tuning, adapters, quantization-aware training).
Proficiency with PyTorch, TensorFlow, Hugging Face; knowledge of MLOps best practices and working with large datasets and cloud ML services.
Experience in applied AI across NLP, computer vision, or multimodal domains with strong problem-solving skills in model adaptation and optimization.
Ability to lead and mentor junior applied scientists and ML engineers, providing technical guidance.
Comfortable translating advanced research into practical, scalable production ML solutions collaborating closely with product, engineering, and business teams.