Cloud Python Developer

Job Title: Cloud Python Developer

Location: Remote

Duration: Contract

Job Description:

Role Overview

• Be a part of core Platform Engineering team and build and manage Infra using Infrastructure as a Code(IaC) on AWS

• Design and solutioning for resiliency, scalability, availability an security of the platform.

• Application alerting and monitoring to enhance application availability, reliability and performance on production using APM/Synthetics/Infrastructure logs.

• Develop automation of the support process for operational tasks for operational aspects of operational problems.

• Ensure Patch management on regular cadence, delivery of tasks – Incidents, tasks, problem ticket.

• Understand security threats and patterns using the tools available AWS WAF/Shape/UID/Re-Captcha/Threat-Metrix/NESS/Pixel Monitoring and alerting.

• Performance, Resiliency & Capacity Testing - Execution, Result review & prepare action items working with development teams.

• Collaborate between development and operations to achieve key business objectives by implementing best practices.

• Active participant in the ownership of the enhancing performance, efficiency, resiliency and monitoring of the software development process.

• Provide release management and post deployment support and to quickly respond to and resolve unexpected service problems in production.

• Tools: GitHub, Github Actions Workflows, AWS, Helm, EKS, Docker, Terraform, Python, MYSQL, Datadog, Opensearch

Required Qualifications:

Undergraduate degree, 5+ years Cloud experience, GitHub, Github Actions Workflows, AWS, Helm, EKS, Docker, Terraform, Python, MYSQL, Datadog, Opensearch

Required AI Skills:

• All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to:

-Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise.

-Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows.

-Continuous Learning: Stay current with evolving AI capabilities and features, and apply them to improve delivery quality and velocity.