Specialist Software Engineer (Automation DRE)
Missions

Responsibilities include:
- Develop and automate complex workflows and systems using Python to streamline IT processes and infrastructure management.
- Design, implement, and manage Ansible playbooks for infrastructure automation, configuration management, and continuous deployment.
- Build and maintain scalable automation solutions within private cloud environments (Openstack) leveraging cloud-native tools and services.
- Collaborate with cross-functional teams to integrate AIOps for proactive system monitoring, anomaly detection, and automated incident response.
- Implement MLOps pipelines to automate machine learning model deployment, monitoring, and lifecycle management in production environments.
- Optimize infrastructure and processes using automation frameworks to reduce operational overhead and improve system performance.
- Automate routine tasks and processes related to system provisioning, configuration, and patch management.
- Design self-healing, auto-scaling systems by incorporating advanced automation techniques in cloud platforms.
- Create automated workflows to manage data pipelines, train models, and monitor ML models' performance in MLOps environments.
- Collaborate with DevOps teams to build and maintain CI/CD pipelines and automated deployment processes for applications and machine learning models.
- Continuously assess and improve automation frameworks and pipelines to align with the latest industry best practices.

Profile

- Python: Strong proficiency in scripting and automation tasks.
- Power shell scripting knowledge.
- Ansible: Expertise in writing and managing Ansible playbooks for infrastructure automation.
- Cloud Knowledge: Sound understanding of cloud platforms like AWS, Azure, or GCP, including serverless architectures and cloud-native automation tools.
- AIOps: Experience with tools and frameworks that use AI to enhance IT operations (such as monitoring, event correlation, and incident management).
- MLOps: Familiarity with automating the deployment, monitoring, and management of machine learning models.
- DevOps & CI/CD: Experience with CI/CD pipelines and infrastructure as code.
- Good technical grasp of databases and systems
- Problem Solving: Strong analytical skills to identify and resolve automation challenges effectively.