Live opening · Posted 12 days ago
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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.
Strong 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
Work arrangement
No
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