Padmi

Data Scientist Generative AI Specialist (Noida)

Delhi NCRPosted 1 month ago
Data Science And StatisticsMid-levelFull Time; Regular
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Job Title: Data Scientist Generative AI Specialist Location: Noida, India Job Type: Full-Time Experience Required: 3+ years in Data Science with a solid focus on Generative AI Budget: Up to 22 LPA Notice Period: Immediate About the Role We are looking for an experienced Data Scientist Generative AI Specialist to design, develop, and optimize cutting-edge AI solutions. The ideal candidate will have hands-on expertise in Generative AI, Agentic AI, LLMs, and advanced NLP, along with experience deploying AI models at scale in enterprise environments. Key Responsibilities - Build, fine-tune, and deploy Generative AI and LLM-based solutions. - Implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, and semantic search. - Develop and manage Knowledge Graphs to enhance reasoning and context. - Apply Prompt Engineering best practices to improve AI output quality. - Work with Azure AI ecosystem Azure OpenAI, Cognitive Services, Azure ML. - Leverage LangChain and other Agentic AI orchestration frameworks for intelligent automation. - Utilize Hugging Face for model training, customization, and deployment. - Integrate and optimize AI models using FastAPI, Databricks, and AWS services. - Manage MLOps pipelines using CI/CD, MLflow, and DevOps tools. - Work with vector databases (Faiss, Redis) for storage and retrieval of embeddings. - Troubleshoot, debug, and improve AI systems for performance and reliability. Required Skills & Experience - 3+ years of Data Science experience, with a focus on Generative AI, Agentic AI, LLMs, and NLP. - Strong Python programming skills for AI application development. - Expertise in RAG, Knowledge Graphs, Prompt Engineering. - Proficiency with Azure OpenAI, Cognitive Services, Azure ML. - Hands-on experience with LangChain, Hugging Face, and vector DBs (Faiss, Redis). - Familiarity with FastAPI, Databricks, AWS, MLflow. - Solid background in MLOps, CI/CD pipelines, and AI deployment optimization. .

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