Role: Data Engineer
Industry Type: IT Services & Consulting
Department: Engineering - Software & QA
Employment Type: Full Time, Permanent
Role Category: Software Development
UG: Any Graduate
Key Responsibilities
Design, implement, and maintain AWS infrastructure supporting production-grade GenAI solutions. Architect cloud solutions using AWS services including: AWS Lambda API Gateway DynamoDB Amazon S3 Amazon Bedrock Amazon SageMaker IAM Develop and maintain Python-based reusable cloud and GenAI platform components. Build, manage, and optimize Infrastructure-as-Code using Terraform. Develop and maintain CI/CD pipelines using GitHub Actions. Implement secure IAM policies, access controls, and governance standards. Monitor cloud environments using CloudWatch, X-Ray, logging, and observability tools. Troubleshoot infrastructure, deployment, security, and application issues. Optimize cloud infrastructure for performance, reliability, scalability, and cost efficiency. Collaborate with Data Science, MLOps, DevOps, and Engineering teams to operationalize GenAI models and services. Develop reusable cloud patterns, architecture standards, and operational documentation. Must-Have Skills AWS Cloud Engineering Strong hands-on experience with: AWS Lambda API Gateway DynamoDB S3 IAM CloudWatch Experience designing and supporting production AWS environments. Generative AI Platforms Experience working with: Amazon Bedrock Amazon SageMaker Understanding of GenAI application deployment and operationalization. Infrastructure as Code Strong expertise in: Terraform Experience building reusable infrastructure modules and automation frameworks. DevOps & CI/CD Strong experience with: GitHub Actions CI/CD pipelines Automated deployments Understanding of DevOps best practices and cloud automation. Programming & Automation Strong proficiency in: Python Experience building automation scripts and cloud platform components. Security & Operations Experience implementing: IAM policies Security controls Monitoring and observability solutions Knowledge of cloud governance and compliance practices. Good-to-Have Skills Experience with: MLOps platforms and workflows Containerization technologies (Docker, Kubernetes) Serverless architectures Exposure to: AI/ML model deployment FinOps and cloud cost optimization AWS certifications preferred.