AI Cloud Architect

Job description

Overview • We are looking for a AI Cloud Architect + developer who can design, develop, and deploy cloud-based solutions for our clients. • You will be responsible for creating scalable, secure, and cost-effective cloud architectures that meet the business and technical requirements of the projects. • You will also be involved in coding, testing, debugging, and troubleshooting cloud applications using various cloud services and tools. • You will work closely with other developers, architects, and project managers to deliver high-quality cloud solutions that meet the client's expectations and deadlines. Responsibilities • Design and develop AI cloud architectures and applications using AWS/ Azure/Google Cloud, or other cloud platforms. • Conduct research and feasibility analysis for GenAI use cases relevant to the customer’s business. • Review offshore deliverables to ensure adherence to best practices and quality standards before client submission. • Collaborate with cross-functional teams to align technical solutions with business goals. • Confidently articulate AI/ML concepts and practices to both technical and non-technical stakeholders. • Integrate cloud solutions with existing systems and applications using APIs, microservices, and other methods. • Optimize cloud resources and costs using automation , monitoring, and analytics tools. • Document and communicate cloud architectures and applications using diagrams, reports, and presentations. • Research and evaluate new cloud technologies and trends to improve the existing cloud solutions and create new opportunities. Qualifications • Bachelor's degree in computer science, engineering , or related field. • At least 8 years of experience in cloud architecture and development using AWS/Azure/Google Cloud, or other cloud platforms. • Proficient in one or more programming languages such as Python, Java, C#, Node.js, etc. • Strong hands-on experience in Python programming. • Exposure to ML Ops practices and deployment workflows. • Knowledge of cloud services and tools such as EC2, S3, Lambda, CloudFormation, Azure Functions, App Service, Storage, Google Compute Engine, Cloud Storage, Cloud Functions, etc. • Knowledge of AI/ML services offered by GCP, Azure, and AWS.