Passion for applying AI and machine learning to solve large societal challenges.
Bachelor's or Master's degree (or equivalent) in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or another quantitative discipline, with 1–4 years of relevant work or research experience.
Minimum 2 years of hands-on experience developing and applying machine learning models in an industry or research setting.
Demonstrated experience working with Large Language Models (LLMs) or foundation models through industry projects, research, or significant open-source contributions.
Strong understanding of deep learning fundamentals, including Transformer architectures, attention mechanisms, and modern representation learning techniques.
Experience with one or more LLM adaptation techniques such as supervised fine-tuning, parameter-efficient fine-tuning (e.g., LoRA/QLoRA), instruction tuning, preference optimization, retrieval-augmented systems, structured generation, or tool-using agents. Experience should extend beyond prompt engineering alone.
Experience designing and implementing evaluation methodologies for LLM systems, including benchmarking, model analysis, and iterative experimentation.
Solid software engineering skills in Python and experience with modern ML frameworks such as PyTorch, Hugging Face Transformers, or equivalent.
Experience building end-to-end ML systems, including data preparation, experimentation, model training, evaluation, and deployment.
Track record of applied machine learning projects addressing real-world problems. Experience applying LLMs to practical use cases is highly desirable.
Experience building and evaluating conversational or multi-turn AI systems is a plus.
Excellent written and verbal communication skills and the ability to work effectively in cross-functional teams.
Benefits
Insurance Benefits - Medical Insurance, OPD Coverage, Accidental Insurance, Life Insurance.
Wellness Program - Employee Assistance Program, Annual Health Check-Ups, Onsite Medical Centers, Emergency Support System.
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