Enterprise Solutions Architect
4 weeks ago
Job Description
- Create architectural solutions and the roadmap for enterprise architecture, taking input and partnership with all the key business and infrastructure partners.
- Contribute to the IT strategy to ensure alignment with enterprise architecture practices and goals.
- Partner with business and technology subject experts to translate business requirements into technological solutions.
- Coordinate, review, and approve architectural solutions to ensure they comply with standards, policies, and guidelines.
- Monitor and review industry, technology, and market trends to figure out their potential impact or application for the business.
- Participate in digital strategy workshops and provide digital business model consulting within IT teams.
- Drive continuous architectural review process and recommend changes to transform services through adopting new and improved architectures.
- Continuously assess industry trends, new concepts, and how they can influence outcomes.
- Review of Generative AI requirements in Infrastructure and Operations.
Requirements
- Bachelor’s or Master’s in business management, computer science, computer engineering, or a related field of study with 10+ years of experience leading and developing architectures for various aspects of the infrastructures.
- Experience in enterprise architecture or application architecture, application development, and systems management
- Thorough knowledge and hands-on experience of various aspects of infrastructure stacks related to network, authentication, compute platforms, storage platforms, architecture frameworks, and public cloud platforms
- Comprehensive knowledge of building, handling, and representing Infrastructure as a Service, Platform as a Service, and Software as a Service
- Ability to think critically and solve problems
- Demonstrated technology skills across multiple IT domains (e.g., Identity & Access Management, Business Applications infrastructure, Security)
- Good knowledge of Generative AI models and frameworks.
- Deep hands-on knowledge of Kubernetes, developing backend platforms and engineering APIs that scale
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