Role: Cloud Platform & AI Engineer

Position Type: Full-Time Contract (40hrs/week)

Contract Duration: Long term

Work Schedule: 8 hours/day (Mon-Fri)

Location: Hybrid - Onsite 2 days a week in Hyderabad

We are seeking a hands-on Cloud Platform & AI Engineer with 4+ years of experience supporting AWS cloud platforms, automation, data-processing workloads, and AI/ML initiatives.

The ideal candidate will have strong experience with AWS, Python, Terraform, Apache Spark/PySpark, Linux, and CI/CD, along with practical exposure to AI/ML and Generative AI technologies.

Key Responsibilities

- Support and enhance AWS cloud platform environments.
- Provision and maintain AWS infrastructure using Terraform.
- Develop Python automation, platform utilities, APIs, and data-processing solutions.
- Administer and troubleshoot Linux environments.
- Provision, configure, and troubleshoot AWS EMR clusters and Spark workloads.
- Develop and optimize Apache Spark/PySpark workloads.
- Monitor cloud environments using AWS CloudWatch.
- Contribute to AI/ML and Generative AI initiatives, including LLM applications, MCP servers, and AI agent workflows.
- Support CI/CD and automated deployments using Jenkins, GitHub,



Bitbucket, or similar tools.
- Assist with cloud security, vulnerability remediation, monitoring, and logging.
- Support containerized and data-science environments as needed.

Requirements

- 4+ years of relevant cloud, platform, software, or data engineering experience.
- Strong hands-on experience with AWS, particularly EC2, S3, IAM, VPC, RDS, EMR, and CloudWatch.
- Strong Python development and scripting experience.
- Strong Terraform / Infrastructure as Code experience.
- Hands-on experience with Apache Spark / PySpark.
- Experience provisioning and troubleshooting AWS EMR clusters.
- Robust Linux administration and troubleshooting skills.
- Practical experience with AI/ML and Generative AI, including LLMs, AI agents, MCP servers, or similar technologies.
- Experience with Jenkins, GitHub, Bitbucket, or comparable CI/CD/source-control tools.

Preferred

- Docker and Kubernetes/EKS
- REST APIs
- Containerized workloads
- Cloud monitoring, logging, and security
- JupyterHub/JupyterLab
- Data science/ML development environments

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