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Medium — financial-brand and mid-level LLM role attracts many applicants but requires ML specialization.
Low — LLM fine-tuning and ML engineering skills are broadly transferable across industries.
High — explicit 3+ years applied ML, LLM fine-tuning experience, Python and PyTorch requirements.
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Contribute to development, fine-tuning, and evaluation of large language models (LLMs) for the GenAI platform.
Design and implement ML experiments and workflows using Python under senior guidance, including fine-tuning techniques like LoRA and QLoRA.
Support benchmarking, performance analysis, anomaly detection, and documentation of generative AI model experiments.
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field.
0–7 years total IT experience with at least 3 years in applied machine learning or data science (including internships or academic research).
Proficiency in Python programming and experience with ML frameworks such as PyTorch or TensorFlow.
Basic understanding of LLM architectures, fine-tuning techniques, evaluation metrics for generative models, and familiarity with cloud AI/ML services (AWS, Azure, or GCP).
Early-career professionals focused on advancing LLM capabilities through applied research and experimentation.
Candidates with hands-on experience in Python-based model training pipelines and familiarity with MLOps practices.
Individuals comfortable working under senior guidance in collaborative model development and evaluation environments.