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Job DescriptionLocation: Delhi Experience: 5+ Years Role OverviewWe are seeking a Senior Python & AI Engineer to build production-grade applications and advanced AI systems. This role demands a strong backend engineer who can seamlessly bridge clean Python application design with cutting-edge AI implementation, including autonomous agents, custom model creation, and LLM fine-tuning. Core ResponsibilitiesBackend Engineering: Build and maintain scalable, production-grade Python backends, high-throughput APIs (FastAPI/Flask), and optimized data pipelines.Agentic AI: Architect and deploy autonomous multi-agent systems using frameworks like LangChain, CrewAI, or AutoGen.LLM & Model Engineering: Fine-tune, evaluate, and deploy open-source and proprietary LLMs alongside optimized Retrieval-Augmented Generation (RAG) pipelines using vector databases.Architecture & Deployment: Own end-to-end integration from local Python environments to cloud-hosted microservices and databases.Technical Stack RequirementsLanguages & Frameworks: Python (expert), FastAPI/Flask, PyTorch/TensorFlow, Hugging Face.AI Orchestration: LangChain, LlamaIndex, or CrewAI.Data & Cloud: SQL/NoSQL, Vector Databases (Pinecone, Milvus, or Chroma), Docker, and cloud infrastructure.EducationalB.E/B.Tech/ MCA Job DescriptionLocation: Delhi Experience: 5+ Years Role OverviewWe are seeking a Senior Python & AI Engineer to build production-grade applications and advanced AI systems. This role demands a strong backend engineer who can seamlessly bridge clean Python application design with cutting-edge AI implementation, including autonomous agents, custom model creation, and LLM fine-tuning. Core ResponsibilitiesBackend Engineering: Build and maintain scalable, production-grade Python backends, high-throughput APIs (FastAPI/Flask), and optimized data pipelines.Agentic AI: Architect and deploy autonomous multi-agent systems using frameworks like LangChain, CrewAI, or AutoGen.LLM & Model Engineering: Fine-tune, evaluate, and deploy open-source and proprietary LLMs alongside optimized Retrieval-Augmented Generation (RAG) pipelines using vector databases.Architecture & Deployment: Own end-to-end integration from local Python environments to cloud-hosted microservices and databases.Technical Stack RequirementsLanguages & Frameworks: Python (expert), FastAPI/Flask, PyTorch/TensorFlow, Hugging Face.AI Orchestration: LangChain, LlamaIndex, or CrewAI.Data & Cloud: SQL/NoSQL, Vector Databases (Pinecone, Milvus, or Chroma), Docker, and cloud infrastructure.EducationalB.E/B.Tech/ MCA
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