Machine Learning Engineer

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