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Mid-level ML role, metro location, popular title and experience band increase candidate competition.
Core ML, NLP and LLM skills transfer across industries, though recruiting-domain knowledge is beneficial.
Explicit 4.5+ years requirement plus mandatory ML/LLM and specific tech stack increases strictness.
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Lead data science projects involving predictive modeling, NLP, statistical analysis, and machine learning to deliver insights and data products.
Collaborate with system architects and product teams to develop scalable data systems and machine learning models improving user experience.
Innovate with prompt engineering, benchmarking Large Language Models, and agentic workflows to solve business problems in recruiting domain.
4.5+ years as Senior Software Engineer or equivalent experience in data science or related roles.
Expert in Python programming and experienced with ML libraries (TensorFlow, PyTorch) and NLP libraries (Spacy, NLTK, Gensim).
Proficient in backend technologies such as Flask/Gunicorn and familiar with software development lifecycle including CI and service-oriented architectures.
Experience in machine learning algorithms including regression, clustering, neural networks, ensemble methods, and knowledge of prompt engineering and vector databases.
Experienced applying advanced ML and NLP techniques to large, complex user data sets with business impact in product development.
Comfortable working cross-functionally with engineering, product, and analytics teams in a data-driven environment.
Hands-on with modern ML ops including prompt engineering, LLM benchmarking, and agentic workflows to enhance user experiences specifically in career/recruiting domains.