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Mid-level ML role, popular Data Scientist title and reputable employer increase candidate density.
Skills in LLMs, MLOps, and knowledge graphs transfer across industries but favor ML-focused backgrounds.
Explicit 5+ years and 3+ years ML/NLP plus mandatory tech stack creates strict filters.
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Own and lead domain-specific AI/ML problem statements within the Agentic AI platform, focusing on applied research and product-oriented data science.
Manage and execute foundational and domain model evaluation, fine-tuning, prompt optimization, and operationalization workstreams including design of evaluation frameworks and anomaly detection.
Collaborate with engineering and product teams to deploy scalable AI solutions and maintain knowledge graphs, embeddings, and inference engines for semantic search and reasoning.
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
Minimum 5+ years overall IT experience with at least 3+ years in applied machine learning or NLP in production environments.
Strong experience with LLMs, GenAI, or NLP, including fine-tuning and evaluation; proficiency in Python and ML libraries such as PyTorch, TensorFlow, Scikit-learn.
Experience with graph databases (e.g., Neo4j, DGL), knowledge graphs, vector stores, and familiarity with cloud AI/ML services (AWS, Azure, or GCP) and MLOps.
Experienced in leading AI/ML projects related to large language models, generative AI, and agentic AI systems within enterprise environments.
Capable of independently managing technical research to product workflows including model evaluation, fine-tuning, and scaling AI deployments.
Comfortable working cross-functionally with engineering, product, and business teams to translate AI research advances into operationalized solutions.