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End-to-End System Architecture:
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Define and design AI and non-AI components of software applications, ensuring modularity, scalability, and security
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Architect solutions that integrate GenAI capabilities into enterprise systems while ensuring seamless interoperability with backend services, APIs, databases, and user interfaces
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Ensure adherence to cloud-native best practices across AWS and Azure deployments, including containerization, orchestration, and infrastructure as code
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AI Model Integration \& Optimization:
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Define robust patterns for utilizing LLMs, multimodal models, and RAG and agentic systems
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Design efficient model-serving pipelines, integrating AI capabilities into existing business workflows
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Enterprise-Grade Software Engineering:
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Ensure that AI applications follow software engineering best practices, including version control, CI/CD, automated testing, and code quality assurance
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Architect secure and scalable APIs to facilitate seamless communication between AI models, databases, and user interfaces
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Design data pipelines that efficiently handle structured and unstructured data, ensuring AI models receive high-quality input data
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Observability, Performance \& Security:
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Define monitoring and logging strategies for AI-driven applications to ensure model performance, API health, and data integrity
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Implement AI observability practices, ensuring visibility into model behavior, drift detection, and anomaly identification
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Design architectures that adhere to data governance, security, compliance, and ethical AI guidelines
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Collaboration \& Governance:
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Work closely with ML Engineers, Data Scientists, Software Engineers, Business Analysts, Product Owners and other members of Agile development teams to translate business requirements into AI-driven architectures
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Provide technical leadership in AI architecture reviews, design discussions, and solution validation
