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Mid-level GenAI role with popular skills and metro context yields moderately high applicant density.
GenAI and RAG skills are transferable across industries but require ML-specific expertise, so moderate sensitivity.
Explicit 3–6 years plus mandated LLM, LangChain, RAG, and vector DB experience sets strict filters.
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Manage and debug production pipelines of Generative AI models, including root cause analysis of defects.
Design and build Large Language Model (LLM)-based workflows using frameworks such as LangChain, including prompt engineering and pipeline orchestration.
Implement Retrieval-Augmented Generation (RAG) architectures utilizing vector databases like Pinecone, and handle document ingestion and unstructured data extraction workflows.
3–6 years of experience in data science, applied machine learning, or Generative AI roles.
Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or a related field.
Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
Practical experience with LLMs, Generative AI frameworks including LangChain, and expertise in RAG architectures and vector databases like Pinecone.
Experienced in managing end-to-end GenAI production pipelines with troubleshooting and optimization focus.
Skilled in designing and orchestrating complex LLM workflows and implementing advanced RAG techniques for semantic search and contextual retrieval.
Able to collaborate effectively with cross-functional teams to define data requirements and communicate technical insights to both technical and non-technical stakeholders.