We are hiring a Python AI Engineer with experience in building enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent frameworks. The ideal candidate should be comfortable designing scalable AI applications, integrating enterprise systems, and delivering production-ready intelligent automation solutions.
Key Responsibilities
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Design and implement agentic AI solutions using LangChain, LangGraph, CrewAI, or similar frameworks.
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Develop multi-agent systems capable of coordinating tasks, tool usage, and workflow execution.
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Build and optimize RAG pipelines, including document ingestion, embedding generation, indexing, and retrieval.
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Integrate foundation models such as GPT, Gemini, Llama, or equivalent into enterprise applications.
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Design connectors and integrations for APIs, databases, enterprise systems, and document repositories using MCP or similar protocols.
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Develop AI-powered applications, dashboards, and user interfaces using modern web technologies.
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Develop secure and scalable data processing workflows supporting AI operations and enterprise governance.
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Monitor, test, tune, and optimize AI agents for performance, accuracy, and reliability.
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Collaborate with business stakeholders to translate requirements into AI-driven solutions.
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Prepare technical documentation, architecture designs, and implementation guides.
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Deliver production-ready agentic AI workflows, RAG-based knowledge retrieval solutions, and enterprise integrations.
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Provide deployment support, performance optimization recommendations, and knowledge transfer documentation.
Required Skills
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Experience with Large Language Models (LLMs) and Generative AI application development.
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Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
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Strong understanding of RAG, embeddings, vector search, and semantic retrieval techniques.
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Proficiency in Python and API integration.
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Experience integrating enterprise data sources and external tools into AI workflows.
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Familiarity with cloud-native platforms and data engineering environments.
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Ability to design scalable, modular, and production-ready AI architectures.