DevOps & Cloud Infrastructure Le...

Copad Pharma -
Cairo, Egypt

DevOps & Cloud Infrastructure Lead

Full Time

On-site

Copad Pharma -
Cairo, EgyptPosted 2 months ago

53Applicants for1 open position

20Viewed

9In Consideration
0Not Selected

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Job Description

Overview

We are looking for a DevOps & Cloud Infrastructure Lead to play a critical role in managing cloud services, CI/CD pipelines, infrastructure scalability, and software deployment. This individual will oversee DevOps best practices, cloud-based AI service integration, and deployment automation for a Python-based PySide6 desktop application.

This is a high-impact, strategic role that ensures smooth collaboration across the development team, reliable software distribution, and efficient deployment of generative AI services. The ideal candidate has strong experience in cloud infrastructure, CI/CD, and software release management, along with a solid understanding of Python-based application packaging, deployment, and scaling.

Key Responsibilities

1. DevOps & Software Deployment (PySide/PyQt Desktop Application)

Design and implement CI/CD pipelines to automate the testing, packaging, and deployment of the PySide/PyQt desktop application.
Develop update mechanisms that allow users to seamlessly upgrade to newer versions.
Manage version control workflows (GitHub/GitLab) and enforce best practices for software integration across the team.
Ensure cross-platform compatibility for packaged desktop applications (Windows, Linux, macOS).

2. Cloud Infrastructure & AI Services

Evaluate, set up, and maintain cloud-based services (AWS, GCP, or Azure) to host APIs, AI models, and other backend services.
Oversee server management, ensuring that APIs used by the desktop app are optimized, secure, and scalable.
Manage and scale infrastructure for generative AI services, including provisioning GPU instances, managing AI inference workloads, and optimizing costs.
Secure cloud environments through appropriate IAM, encryption, and networking configurations.

3. API & Backend Deployment

Design, deploy, and maintain server-side APIs (FastAPI, Flask) to support communication between the desktop app and cloud-based AI services.
Ensure API efficiency, security, and monitoring, optimizing request handling and data transfer between client and server.
Integrate with third-party APIs when required and manage API authentication mechanisms.

4. Software Testing & Performance Optimization

Implement automated testing strategies for both backend services and PySide/PyQt application updates.
Monitor system performance, debugging infrastructure issues, and optimizing cloud-based workloads for AI model inference.
Design logging, monitoring, and alerting systems to track application health and deployment stability.

5. Security, Scalability & Infrastructure as Code

Establish data security and protection measures for cloud-stored data and API interactions.
Ensure scalability of cloud infrastructure as the user base grows.
Utilize Infrastructure as Code (IaC) tools such as Terraform or CloudFormation to manage infrastructure efficiently.

6. Collaboration & Documentation

Work closely with developers, AI engineers, and UI/UX designers to ensure infrastructure aligns with software needs.
Write technical documentation outlining system architecture, deployment procedures, and best practices.
Provide guidance to the team on best practices in software deployment, DevOps workflows, and cloud scalability strategies.

Job Requirements

1. DevOps & Infrastructure Expertise

Proven experience setting up CI/CD pipelines for Python-based applications (e.g., GitHub Actions, GitLab CI, Jenkins).
Strong understanding of software versioning, testing pipelines, and automated deployment strategies.
Experience with Infrastructure as Code (Terraform, CloudFormation, Ansible).
Knowledge of containerization (Docker, Kubernetes) and orchestration tools for scalable deployments.

2. Cloud Services & AI Infrastructure

Deep familiarity with AWS, GCP, or Azure, including compute services, storage, and networking.
Experience provisioning GPU instances for AI model inference/training and optimizing cloud workloads.
Knowledge of load balancing, auto-scaling, and cloud cost management.

3. Python Application Deployment & API Development

Experience packaging and deploying Python desktop applications (PyInstaller, Briefcase, or similar tools).
Hands-on experience managing APIs using FastAPI, Flask, or Django, including authentication, security, and optimization.
Familiarity with SQL and NoSQL databases (PostgreSQL, MySQL, MongoDB) for managing backend data.

4. Security & Performance Monitoring

Understanding of application security principles, API security, and data protection best practices.
Experience implementing logging, monitoring, and alerting solutions (e.g., ELK Stack, Prometheus/Grafana).

5. Strong English Proficiency

Written: Ability to create detailed documentation, DevOps workflows, and security policies.
Spoken: Comfortable explaining infrastructure decisions and best practices to developers, data scientists, and non-technical stakeholders.

Nice-to-Have (Not Required but Beneficial)

Experience with machine learning pipelines (MLOps) and AI model deployment best practices.
Familiarity with front-end frameworks (React, Vue) to understand full-stack application deployments.
Experience implementing distributed computing solutions for large-scale AI workloads.

Candidate Profile

Research-Driven & Explorative: Actively keeps up to date with the latest DevOps, cloud computing, and AI deployment technologies. Continuously explores new tools, methodologies, and best practices to enhance infrastructure efficiency, security, and scalability. Eager to experiment with cutting-edge cloud solutions and optimize deployment pipelines to ensure the best possible software development and delivery experience.
Highly Organized & Strategic: Capable of managing complex deployment workflows and ensuring smooth software releases. Understands the broader architectural impact of DevOps decisions and can design scalable, future-proof infrastructure solutions.
Collaborative & Team-Oriented: Works closely with software engineers, AI specialists, and designers to integrate infrastructure solutions that align with both technical and business needs. Ensures that DevOps best practices enhance, rather than hinder, development velocity.
Problem-Solver: Anticipates challenges in cloud scalability, security, performance optimization, and deployment efficiency. Proactively implements solutions to mitigate risks and maintain system reliability.
Ownership Mindset: Takes full responsibility for system stability, security, automation, and performance. Ensures a seamless developer experience and a scalable cloud environment that supports both desktop and AI-driven applications.