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This is a hands-on engineering role focused on building production-ready AI platforms from architecture through deployment.\nThe ideal candidate has extensive experience developing scalable AI solutions, integrating LLMs into enterprise applications, and delivering secure, reliable systems that operate in production environments.\nResponsibilities\nDesign and develop enterprise Generative AI applications using LLMs, RAG, Graph RAG, and multi-agent architectures.\nBuild scalable document ingestion, embedding, retrieval, and vector search pipelines.\nDevelop AI agents using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, AutoGen, or similar technologies.\nCreate secure backend services and APIs using Python, FastAPI, Flask, or comparable frameworks.\nBuild intuitive AI-powered web applications using modern front-end technologies such as React, Angular, or Next.js.\nDeploy cloud-native AI solutions across AWS, Azure, and GCP using Docker, Kubernetes, and Infrastructure-as-Code.\nImplement observability, monitoring, LLMOps, and governance to ensure production reliability and responsible AI practices.\nCollaborate with product, engineering, architecture, and business stakeholders to deliver enterprise AI solutions.\nRequired Qualifications\n8+ years of software engineering, cloud engineering, AI/ML, or platform engineering experience.\n3+ years of hands-on experience building production Generative AI or LLM-based applications.\nStrong expertise with Python and API development using FastAPI, Flask, or similar frameworks.\nExperience building Retrieval-Augmented Generation (RAG) solutions and working with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.\nExperience with Agentic AI frameworks including LangChain, LangGraph, CrewAI, LlamaIndex, Semantic Kernel, or AutoGen.\nStrong understanding of prompt engineering, tool calling, agent orchestration, and workflow automation.\nExperience developing cloud-native applications on AWS, Azure, or GCP.\nHands-on experience with Docker, Kubernetes, Terraform, CI/CD pipelines, and modern DevOps practices.\nExperience integrating enterprise AI applications with databases, APIs, and business systems.\nStrong understanding of security, governance, and responsible AI best practices.\nPreferred Qualifications\nExperience implementing Graph RAG and knowledge graph solutions.\nExperience with MCP (Model Context Protocol) architecture.\nExperience deploying models using vLLM, Hugging Face, Triton, or TensorRT-LLM.\nExperience with Databricks, Spark, Kafka, Snowflake, or modern data engineering platforms.\nExperience building AI applications within regulated industries such as Financial Services, Healthcare, or Insurance.\nAzure, AWS, Google Cloud, or Databricks AI certifications.\nTechnical Environment\nLanguages: Python, JavaScript/TypeScript, SQL\nFrameworks: LangChain, LangGraph, CrewAI, LlamaIndex, FastAPI, Flask, React, Angular, 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