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Tier-1 brand, mid-level ML/LLM role, broad skillset and metro hiring amplify competition.
Core ML/NLP skills are transferable, but clinical healthcare domain increases domain specificity.
Explicit 1.5–4 years requirement and core ML/NLP skills required but not excessively niche.
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Develop and maintain NLP and LLM systems specifically for healthcare and oncology clinical text processing workflows such as patient-trial matching and real-world evidence abstraction.
Build, evaluate, and improve prototype and production-level components including prompt engineering, retrieval-augmented generation (RAG) pipelines, fine-tuning, and extraction logic.
Collaborate cross-functionally with Clinical AI Data Specialists and ML Evaluation Engineers to improve data quality, model robustness, and conduct thorough error analysis.
1.5–4 years of professional experience in machine learning, NLP, LLMs, or data-centric software engineering.
Strong proficiency in Python programming with experience handling complex, real-world data sets.
Familiarity with PyTorch, HuggingFace transformers, embeddings, prompt engineering, RAG methodology, and fine-tuning techniques.
Understanding of ML evaluation metrics (precision, recall, F1), proper train/validation/test protocols, overfitting, error analysis, and experiment tracking.
Experience working hands-on with clinical or biomedical NLP projects involving healthcare documents or information extraction.
Operates with strong technical ownership of assigned modules; able to independently develop, test, and document reliable code and experiments.
Effective collaborator across multidisciplinary teams including clinical data specialists and evaluation engineers, focused on measurable model quality improvements.