Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 employer, popular ML/LLM role, metro location and mid-level generalist requirements increase applicant competition.
Core ML and LLM skills are transferable, but healthcare data and production MLOps add moderate domain sensitivity.
Many mandatory ML/LLM frameworks, MLOps and production deployment requirements plus healthcare context raise screening rigor.
Design, develop, train, evaluate, deploy, and maintain machine learning and deep learning models, including LLM-powered applications and workflows.
Build and maintain robust data pipelines for data collection, cleaning, and preprocessing for model training and deployment.
Implement MLOps best practices for model lifecycle management, evaluation frameworks, and reliability of generative AI systems including prompt engineering and output validation.
Hands-on experience with ML frameworks like TensorFlow, PyTorch, Keras, or Scikit-learn.
Experience integrating LLM APIs (Anthropic Claude, OpenAI, Azure OpenAI) and building LLM workflows using orchestration frameworks (LangGraph, LangChain).
Proficiency in Python, R, or Java programming; experience with SQL and data processing tools such as Pandas and NumPy.
Work Experience Required: Not explicitly mentioned in the JD.
Deep understanding of machine learning, deep learning, statistical modeling, and core generative AI concepts like RAG, prompt engineering, and embeddings.
Familiarity with cloud platforms (AWS, Google Cloud, Azure), Databricks, and MLOps principles for production deployment and monitoring.
Experienced in building production-grade AI solutions involving LLM reliability, evaluation tooling, and structured, type-safe output validation using Pydantic.