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Mid-level title and known firm increase applicant density, but specialized LLM/graph requirements narrow the pool.
Requires specialized LLM, knowledge-graph, and production AI experience, limiting cross-industry transferability.
Explicit years requirement plus mandatory LLM/NLP, graph DB, and MLOps production experience make filters highly strict.
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Own AI/ML problem statements and deliver agentic AI workflows with domain-specific LLM solutions.
Lead model evaluation, fine-tuning, prompt optimization, and design evaluation frameworks for benchmarking and anomaly detection.
Build and maintain knowledge graphs, embeddings, and collaborate with engineering to deploy scalable production AI solutions.
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
5+ years overall IT experience, with 3+ years in applied machine learning or NLP in production environments.
Strong experience with LLMs, generative AI, fine-tuning, evaluation, and proficiency in Python and ML libraries (PyTorch, TensorFlow, Scikit-learn).
Experience with graph databases (e.g., Neo4j, DGL), knowledge graphs, vector stores, cloud AI/ML services (AWS, Azure, GCP), and MLOps.
Experienced in managing foundational and domain-specific LLM projects with measurable impact on business units.
Skilled in cross-functional collaboration involving product, engineering, and business teams in complex AI/ML environments.
Strong technical competence in generative AI systems, model evaluation, knowledge graphs, and operationalizing AI at production scale.