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Mid-level data scientist title but specialized ML/LLM skills limit broad applicant competition.
Role requires deep ML/LLM expertise, making cross-industry transferability limited and domain-sensitive.
Explicit 4–9 years plus mandatory ML, LLM, Python, SQL and cloud experience makes filters stringent.
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Lead AI/ML projects encompassing development of supervised, unsupervised, deep learning, NLP, and LLM models.
Manage end-to-end predictive modeling lifecycle including data analysis, feature engineering, model building, validation, operationalization, and scalability.
Work on fine-tuning LLMs, prompt engineering, RAG applications, and vector databases; deploy AI/ML solutions aligned with business needs.
4 to 9 years of experience in data science or related roles.
Strong programming skills in Python and SQL with proficiency in cloud platforms like AWS or Azure.
Hands-on experience with machine learning algorithms, deep learning, NLP, and LLM fine-tuning.
Work Experience Required: 4 to 9 years
Experienced in complex AI/ML model development and deployment, including transformer architectures and RAG applications.
Comfortable working with emerging AI tools and frameworks such as PyTorch, LangChain, Langgraph, and autonomous agent orchestration.
Skilled at translating technical findings into actionable insights for diverse business stakeholders to drive AI/ML adoption.