DevOps Engineer - AWS/Azure Cloud Services (3-7 yrs)

Job Description :

Responsibilities :

- DevOps Automation & Infrastructure Management : Design, implement, and manage CI/CD pipelines using Terraform, Jenkins, Docker, and Kubernetes to streamline deployment processes.

- Cloud Infrastructure : Oversee the setup and optimization of cloud infrastructure (AWS, GCP, Azure) to support large-scale application deployment and monitoring.

- MLOps Support : Collaborate with data scientists and machine learning engineers to implement MLOps practices, facilitating model retraining, scaling, and monitoring in production

environments.

- Monitoring & Troubleshooting : Set up monitoring solutions for infrastructure and applications, ensuring timely identification and resolution of production issues.

- Security & Compliance : Ensure all systems adhere to security and compliance standards, particularly around data privacy and cloud security best practices.

- Collaboration : Work closely with cross-functional teams to ensure smooth integration of applications and machine learning models into production systems.

- Documentation & Version Control : Maintain detailed documentation for all DevOps and MLOps workflows, configurations, and automation scripts to ensure reproducibility and transparency.

- Innovation : Stay current with the latest DevOps and cloud infrastructure trends, and drive the adoption of new tools and practices that can improve efficiency and reliability.

Requirements :

- Education : Bachelor's degree in computer science, engineering, or a related field.

- DevOps Expertise : Minimum of 3 years of experience in building and maintaining CI/CD pipelines, container orchestration (Kubernetes, Docker), and automated deployments in cloud environments (AWS, GCP, Azure).

- Cloud Architecture : Strong understanding of scalable cloud infrastructure for application workloads, including resource management, cost optimization, and high availability.

- Networking & amp; Security : Solid background in networking principles and security practices, with experience implementing best practices and compliance checks in cloud environments.

- Automation Skills : Experience with automating workflows, ensuring ease of use and reproducibility across teams.

- MLOps Familiarity: Understanding of MLOps principles and experience with deploying and managing machine learning models and large language models (e. g., Llama 2 Mistral) in production, including familiarity with tools and frameworks that support model lifecycle management is a plus.

- Monitoring & Debugging : Experience with monitoring tools (e. g., Prometheus, Grafana, ELK stack) and diagnosing infrastructure or application performance issues.

- Strong Collaboration : Excellent communication skills with the ability to work cross-functionally with various teams.

- Continuous Learning : Eagerness to stay updated on DevOps, cloud, and MLOps advancements to drive innovation.