AI Cloud Architect

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 7 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.