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Metro location, popular ML role, and broad skillset demand create moderate candidate competition.
Core ML/NLP skills are transferable, but legal-domain and transformer specialization increases role specificity.
Explicit >8 years overall, >5 years ML, transformer, cloud and stack requirements create strict filters.
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Own and architect end-to-end machine learning and AI solutions aligned with business goals.
Lead and manage multiple ML projects, coordinating cross-team collaboration with Engineering and Product Management.
Drive scalability, performance, and reliability of ML models in production while mentoring technical teams.
Min 8 years overall industry experience with 5+ years in AI/ML solutions including design, development, and deployment of ML models.
Proficient in Python and ML libraries/frameworks: TensorFlow, PyTorch, XGBoost, Scikit-learn, Spark ML; experience with code/model versioning tools like Git and MLFlow.
Experience on cloud platforms (Azure, Databricks) and data analytics tools (Spark, Pandas).
Hands-on with transformer models (BERT, RoBERTa, LegalBERT, DeBERTa) and relevant NLP techniques (Named Entity Recognition/Ontology or Document search or Recommendation systems).
Experienced in leading end-to-end ML/AI initiatives and bridging technical implementation with project leadership responsibilities.
Strong collaboration skills evidenced by working closely with Product Managers, data scientists, and cross-functional teams to align ML work with business needs.
Background or interest in NLP for the legal domain and advanced knowledge of AI/ML ethics and best practices, with preference for Ph.D. in ML.