Employment Time: Full-Time
Cloudelligent is a Cloud-native consultancy and AWS Advanced consulting partner! We specialize in providing bespoke cloud solutions to the Startups, SMB & enterprise segment. Being a next-gen cloud service provider, Cloudelligent helps businesses to make the most out of their cloud investment. We have an international footprint with a diverse team of domain experts, and we are customer- obsessed.
As an AWS Solutions Architect- AI/ML at Cloudelligent, you will be responsible for designing and implementing scalable, innovative cloud solutions for our clients. You will focus on cloud modernization, data engineering, and AI/ML integration to drive business transformation. You will collaborate with clients, service delivery managers, and cross-functional teams to understand business goals, assess existing infrastructure, and develop cloud architectures using AWS technologies.
Note: This position is 100% remote, with up to 20% travel.
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Architect cloud-native solutions and lead app modernization initiatives to migrate legacy applications to AWS, improving scalability, performance, and cost efficiency.
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Design and implement cloud solutions that integrate data engineering, Generative AI, and machine learning (ML) technologies to enhance business operations, customer experiences, and automation.
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Lead technical presales discussions, collaborating with sales teams to understand client needs and design cloud solutions that leverage AI, data, and modernization strategies.
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Act as a trusted advisor, guiding clients through the design and implementation of custom cloud architecture that meet their business goals, with a focus on scalability, security, and cost optimization.
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Lead deep-dive architecture sessions with clients, proposing Well-Architected solutions that include data pipelines, cloud-native frameworks, and modern technologies for app modernization, AI/ML, and data-driven applications.
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Define and implement cloud governance frameworks to ensure compliance with AWS security policies, industry standards, and best practices for data security, AI privacy, and regulatory compliance.
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Collaborate with internal teams to ensure smooth knowledge transfer from pre-sales to delivery teams, ensuring project goals, timelines, and expectations are clearly set.