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This role will drive architecture, best practices, and innovation across voice AI systems.\nResponsibilities\nKey Responsibilities\nArchitect and develop AI-driven IVR and conversational systems using Python\nBuild NLP models for text classification and clustering to support intent detection and call routing\nPerform data analysis using SQL\nDesign and deploy LLM workflows (prompt engineering, RAG, evaluation) for voice and chat use cases\nLead development of agentic AI workflows for multi-step customer interactions\nEstablish LLMOps best practices, including monitoring, evaluation, and lifecycle management\nPartner with IVR platform, speech engineering, and product teams\nMentor offshore and junior AI engineers\nTechnical requirements\n1+ years of experience in AI / ML\nVery strong proficiency in Python\nProven expertise in NLP, including:\no Text classification\no Clustering\nStrong experience in data analysis using SQL\nHands-on experience with LLMs:\no Prompt engineering\no Evaluation frameworks\no Retrieval-Augmented Generation (RAG)\nGood to Have\nExperience with Agentic AI and multi-agent orchestration\nExperience with LLMOps / MLOps in production environments\nCloud Experience\nLibraries & Tools\nPython / ML: NumPy, Pandas, Scikit-learn, XGBoost\nNLP: spaCy, Hugging Face Transformers, NLTK, Sentence-Transformers\nLLM Frameworks: LangChain, LlamaIndex, OpenAI SDK\nAgentic AI (Good to Have): LangGraph, AutoGen, CrewAI\nData & SQL: Oracle, MySQL, SQLAlchemy\nVector Databases: FAISS, Pinecone, Weaviate, Qdrant\nLLMOps / MLOps (Good to Have): MLflow, Weights & Biases, BentoML, Airflow\nPreferred skills\nTechnology->OpenSystem->Python - OpenSystem->Python,Technology->AI-Agentic AI->Agent Engineering,Technology->AI-Traditional AI->NLP-Text Analytics\nEducation\nMaster Of Comp. Applications,Master Of Technology,Bachelor Of Comp. 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