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8–10 years of overall experience in AI/ML and software development.
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Minimum 3+ years of hands-on experience in Python programming.
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Strong experience implementing real-world GenAI projects.
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Hands-on experience with LLM fine-tuning and RAG architecture.
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Experience building enterprise-level Agentic AI solutions.
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Strong knowledge of Agentic AI frameworks such as LangGraph, Google ADK, and CrewAI.
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Understanding of open-source and OpenAI LLM models.
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Strong experience with RAG pipelines and architecture.
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Experience with Vector Databases, embeddings, and semantic search.
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Experience implementing Document AI, OCR, and data extraction solutions.
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Experience generating structured outputs from unstructured documents.
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Databricks integration experience is mandatory.
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Exposure to supervised, unsupervised, and reinforcement learning techniques.
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Experience with Deep Learning and Natural Language Processing (NLP).
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Familiarity with NLP research, industry trends, conferences, and code repositories.
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Experience with Streamlit, Flask, and FastAPI.
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Strong knowledge of REST APIs and API integrations.
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Experience with Docker containers and application deployment.
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Proficiency in Python, PyTorch, SQL, and data analysis libraries.
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Hands-on experience with OpenAI APIs and related libraries.
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Experience developing AI solutions on Azure, AWS, or Google Cloud Platform.
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Strong understanding of LLM deployment and production implementation.
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Excellent analytical, troubleshooting, and problem-solving skills.