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This role requires a seasoned technical leader with over 8 years of architecture experience and deep knowledge of Azure and/or AWS cloud ecosystems, combined with strong capabilities in containerization, orchestration, observability, and modern AI frameworks.\nYou will play a critical role in shaping next-generation AI systems, leveraging Generative AI and Agentic AI paradigms to build scalable, intelligent, and action-oriented solutions.\nKey Responsibilities\nArchitect and develop scalable, high-performance Generative AI solutions on Azure and/or AWS platforms\nDesign, build, and manage containerized AI applications using Docker and orchestrate deployments via Kubernetes\nEstablish and maintain observability frameworks for LLMs, including performance monitoring, system health, and usage analytics\nDevelop and implement machine learning models using Python or R, leveraging frameworks such as TensorFlow, PyTorch, or scikit-learn\nBuild and deploy AI/ML solutions using cloud-native services such as AWS SageMaker, Azure AI, or Google Vertex AI\nDesign and implement multi-agent systems and Retrieval-Augmented Generation (RAG) architectures\nDefine and enforce standards for prompt engineering, vector search, and model orchestration\nBuild and manage enterprise-grade MLOps pipelines covering model training, fine-tuning, evaluation, deployment, and monitoring\nCollaborate with cross-functional teams to integrate AI capabilities into enterprise products and platforms\nLead system optimization, performance tuning, and troubleshooting of AI-driven applications\nEstablish best practices for secure, scalable, and maintainable AI architectures\nMentor and guide AI engineering teams on emerging technologies and implementation best practices\nStay current with advancements in Generative AI, Agentic AI, and cloud technologies\nRequired Skills & Qualifications\nMinimum 8+ years of experience in a technical architecture role\nProven expertise in Azure and/or AWS cloud architectures\nStrong hands-on experience with Docker and Kubernetes\nProficiency in Python or R with experience in TensorFlow, PyTorch, or scikit-learn\nDeep knowledge of MLOps practices and model deployment on AWS SageMaker, Azure AI, or Google Vertex AI\nExperience with multi-agent orchestration and RAG architectures\nSolid understanding of prompt engineering, vector search, and LLM observability frameworks\nExperience with Spring Boot and microservices architecture\nStrong understanding of enterprise AI security, scalability, and governance principles","description_format":"text","description_chars":2909,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital 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