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The ideal candidate will have strong expertise in Python, AWS, and Azure, and experience building scalable AI-driven solutions using modern AI frameworks and cloud services.\nThis role combines hands-on development, architecture design, and technical leadership in delivering intelligent, autonomous, and multi-agent systems for enterprise use cases.\nKey Responsibilities\nAgentic AI & LLM Development\nDesign and implement agent-based AI systems using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar.\n\nDevelop multi-agent orchestration systems with memory, planning, and tool usage capabilities.\n\nIntegrate LLMs (OpenAI, Azure OpenAI, Anthropic, open-source models) into enterprise workflows.\n\nImplement RAG (Retrieval-Augmented Generation) pipelines using vector databases.\n\nBuild secure, production-grade prompt engineering frameworks.\n\nPython Development\nDevelop scalable backend services using Python (FastAPI, Flask, Django).\n\nBuild AI microservices and APIs for enterprise integration.\n\nImplement async processing, workflow automation, and event-driven systems.\n\nEnsure high-quality code with unit tests, CI/CD integration, and best practices.\n\nCloud Architecture (AWS & Azure)\nDesign and deploy AI solutions on AWS:\nSageMaker\n\nBedrock\n\nLambda\n\nECS/EKS\n\nAPI Gateway\n\nDynamoDB / RDS\n\nImplement AI workloads on Azure:\nAzure OpenAI\n\nAzure ML\n\nAzure Functions\n\nAzure Kubernetes Service (AKS)\n\nCognitive Services\n\nBuild secure cloud architectures with IAM, VPC, networking, and encryption best practices.\n\nImplement infrastructure as code (Terraform, CloudFormation, Bicep).\n\nDevOps & MLOps\nSet up CI/CD pipelines for AI applications.\n\nManage containerization using Docker & Kubernetes.\n\nImplement model monitoring, logging, and performance optimization.\n\nEnsure governance, compliance, and responsible AI practices.\n\nLeadership & Strategy\nLead AI architecture discussions and solution design workshops.\n\nMentor junior developers and AI engineers.\n\nDefine AI adoption strategy and roadmap.\n\nCollaborate with cross-functional teams (Product, Cloud, Security, Data Engineering).\n\nRequired Skills & Qualifications\nTechnical Skills\nStrong expertise in Python (8+ years preferred).\n\nHands-on experience with LLMs and agent frameworks.\n\nDeep understanding of:\nPrompt engineering\n\nRAG architecture\n\nVector databases (Pinecone, FAISS, Weaviate, Azure AI Search)\n\nAPI integrations\n\nExperience with AWS and Azure cloud services.\n\nStrong knowledge of REST APIs, microservices, and event-driven architecture.\n\nExperience with Git, CI/CD, Docker, Kubernetes.\n\nCloud & AI Expertise\nExperience deploying AI systems in production environments.\n\nFamiliarity with AI governance, model evaluation, and monitoring.\n\nExperience integrating enterprise data sources (SharePoint, SAP, CRM, 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