Enterprise Data Architect

Job Title: Enterprise Architect – Data & AILocation: BangaloreExperience: 12+ YearsJob SummaryWe are seeking a seasoned Enterprise Architect – Data & AI to design and drive scalable, secure, and business-aligned data and AI architectures. This role will work closely with business leaders and cross-functional technology teams to guide enterprise-wide AI initiatives, ensure strong governance, and enable modern cloud-based data platforms.Key ResponsibilitiesArchitecture & Strategy- Align enterprise data and AI architecture with business strategy, security, and compliance requirements. - Collaborate with infrastructure, security, and application teams to define solution architectures and technology roadmaps. - Evaluate and recommend platforms, tools, and frameworks based on scalability, performance, and cost efficiency. - Provide architectural leadership and drive key technical decisions across multiple programs.AI & Data Architecture- Define and guide AI architecture, including model hosting, inference strategies, scalability, and performance optimization. - Ensure AI solutions comply with responsible AI principles, data privacy, and ethical standards. - Enable seamless integration of AI and data solutions into existing enterprise systems and business workflows.Cloud & Platform Expertise- Design and implement cloud-based data and AI solutions using Azure, AWS, or GCP, with a strong focus on native AI and data services. - Drive adoption of DevOps / MLOps practices, CI/CD pipelines, and automation best practices across teams.Governance & Security- Establish and enforce architecture governance, design reviews, and design assurance practices. - Embed security, privacy, and compliance controls (IAM, data protection, regulatory standards) into data and AI architectures.Technical & Professional Skills- Strong foundation in enterprise and IT architecture frameworks such as TOGAF or equivalent. - Solid understanding of AI/ML concepts, including ML models, NLP, Generative AI, model training, and inference. - Deep expertise in data architectures, including ETL, data pipelines, feature stores, data lakes, and analytics platforms. - Hands-on experience with APIs, microservices, and enterprise integration patterns. - Excellent ability to translate business requirements into scalable technical architectures. - Strong stakeholder communication skills across technical and non-technical audiences.

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