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and other generative AI tools.\n·The ideal candidate will be able to design, develop, and deploy AI-powered solutions that enhance productivity, accelerate workflows, and drive innovation.\n·Healthcare industry experience is a strong plus.\nKey Responsibilities:\nDesign and implement AI-driven solutions leveraging Gemini 2.5, Copilot, Glean, and other LLM-based platforms.\n\nIntegrate generative AI tools into enterprise workflows to improve knowledge management, search, and automation.\n\nCollaborate with product, engineering, and data science teams to build scalable AI applications.\n\nFine-tune and optimize AI/LLM models for specific business and healthcare use cases.\n\nConduct experiments, perform benchmarking, and ensure high-quality AI deliverables.\n\nEnsure compliance, security, and ethical usage of AI in production.\n\nStay up to date with the latest advancements in AI/LLMs, RAG, knowledge graphs, and healthcare AI trends.\n\nRequired Skills & Experience:\nProven hands-on experience with Gemini 2.5, GitHub Copilot, Glean, or similar LLM-based tools.\n\nSolid understanding of LLM architectures, embeddings, and retrieval-augmented generation (RAG).\n\nProficiency in Python, JavaScript/TypeScript, or similar programming languages.\n\nExperience with cloud platforms (AWS, Azure, GCP) and AI/ML deployments.\n\nFamiliarity with Vector Databases (Pinecone, Weaviate, FAISS, Milvus, etc.).\n\nKnowledge of ML pipelines, APIs, and prompt engineering.\n\nExcellent problem-solving, communication, and collaboration skills.\n\nPreferred Qualifications:\n\nHealthcare industry background or prior work on AI/ML healthcare projects(strong plus).\n\nExperience with enterprise search systems and AI-driven knowledge management tools.\n\nExposure to LangChain, LlamaIndex, Hugging Face, OpenAI API, or similar frameworks.\n\nHands-on with AI copilots for development, clinical data processing, or decision support.\n\nStrong understanding of data privacy and compliance (HIPAA, 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