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Mid-level, popular GenAI role in a metro with common 3–6yr band increases applicant competition.
Specialized GenAI, LLM, RAG, and LangChain requirements make cross-industry fit limited and domain-specific.
Explicit 3–6 years plus mandatory GenAI, LangChain, and vector DB experience raises filtering strictness.
Own and manage end-to-end production pipelines for Generative AI models, including debugging and root cause analysis.
Design and build LLM-based workflows using frameworks like LangChain, including prompt engineering and pipeline orchestration.
Implement Retrieval-Augmented Generation (RAG) architectures using vector databases such as Pinecone and handle unstructured document ingestion and 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 related field.
Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
Practical experience with LLMs, GenAI frameworks, LangChain, prompt engineering, RAG architectures, and vector databases like Pinecone.
Experienced in managing complex GenAI workflows and production pipelines with measurable impact.
Strong technical expertise in LLMs, prompt engineering, and vector database utilization for semantic search and contextual data retrieval.
Able to work cross-functionally to align technical solutions with business needs and mentor junior team members.