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Tier-1 employer, mid-level generalist ML role, and metro location drive high competition.
ML/LLM skills transfer across industries, though healthcare domain experience is beneficial.
Explicit 3+ years, mandatory ML/LLM production experience, and specific tech stack requirements raise filter strictness.
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Design, develop, and deploy advanced data science and AI solutions using machine learning and large language models (LLMs).
Build predictive models and AI-powered applications such as AI assistants and Retrieval-Augmented Generation (RAG) pipelines to support data-driven decision making.
Partner with data engineering teams to ensure scalable, production-ready AI systems and communicate findings effectively across technical and non-technical stakeholders.
3+ years of experience in data science, machine learning, or applied AI roles with proven deployment of ML or LLM-powered systems in production.
Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or related discipline.
Strong proficiency in Python for AI development and experience with machine learning, modern data platforms (e.g., Databricks), and building LLM applications.
Experience working with large-scale structured and unstructured datasets including healthcare or clinical data is highly desirable.
Experienced in building and scaling AI/ML systems within healthcare, life sciences, or biopharma domains, familiar with clinical data and healthcare datasets.
Skilled in developing multi-step AI workflows and LLMOps practices including deployment, monitoring, prompt engineering, and experiment tracking.
Able to collaborate cross-functionally translating complex AI outputs into actionable business insights for both technical and non-technical audiences.