7 Key Roles and Responsibilities of a DevOps Engineer

A significant transformation has taken place in the job description of a DevOps engineer in recent years. As cloud-native technologies and AI-driven tools gain prominence, DevOps engineers are now expected to possess a deeper understanding of specific cloud platforms. Their responsibilities have expanded beyond traditional tasks, with a stronger focus on security, automation, and collaboration with development teams.

In this blog, we will delve into the core responsibilities and expectations of today’s DevOps engineers, highlighting the evolving skills and practices required to succeed in this dynamic field.

What is a DevOps Engineer?

A DevOps engineer is a technology specialist who bridges the gap between software development and IT operations. They focus on automating processes, enhancing collaboration, and ensuring seamless software deployments. By leveraging CI/CD (Continuous Integration and Continuous Deployment) pipelines, they accelerate software delivery and minimize downtime. 

In today’s AI-driven landscape, a DevOps engineer plays a crucial role in managing AI/ML model deployments. They work closely with data scientists and software engineers to ensure that machine learning models are integrated into production environments efficiently. Optimizing computing resources and managing cloud-based AI solutions.

Additionally, DevOps engineers are increasingly expected to actively participate in security practices. By incorporating security into the early stages of development (a "shift left" approach), they ensure that vulnerabilities are detected and addressed before deployment. DevOps engineers integrate automated security scans into CI/CD pipelines, making security an ongoing process rather than a final checkpoint. They work alongside security teams to ensure compliance with industry standards and secure AI workloads in production.

DevOps engineers must also stay updated with the latest trends in containerization, serverless computing, and AI-powered automation tools. As industries continue to adopt AI and deep learning technologies, these professionals must ensure that infrastructure supports scalable, reliable, and secure AI workloads.

As reported by Imarc, the DevOps industry is on a rapid upward trajectory, with market value expected to rise from $13.2 billion in 2024 to a staggering $81.1 billion by 2028. This significant growth underscores the increasing reliance on DevOps practices in streamlining software development and enhancing IT operations.

DevOps Engineer Job Description - 7 Key Roles & Responsibilities

1. AI-Driven Automation and Infrastructure Management

Modern DevOps engineers are responsible for automating infrastructure provisioning using AI-powered tools. They utilize Infrastructure as Code (IaC) frameworks like Terraform and Ansible to ensure scalable and repeatable deployments. AI-driven automation reduces manual intervention, enhances efficiency, and minimizes errors in cloud and on-premise environments.

DevOps engineers optimize infrastructure for deep learning workloads, ensuring high availability and efficient resource allocation. They work with GPU-enabled cloud platforms and container orchestration systems like Kubernetes to support AI models in production. Their expertise in infrastructure scaling allows businesses to handle AI-driven applications with minimal latency.

2. CI/CD Pipeline Optimization for AI Models

Continuous Integration and Continuous Deployment (CI/CD) is critical for DevOps engineers, especially when working with AI models. They design and maintain robust CI/CD pipelines that streamline the integration of new AI algorithms into production environments. Automation tools like Jenkins, GitHub Actions, and GitLab CI/CD help them ensure rapid and reliable software delivery.

Furthermore, DevOps engineers implement monitoring systems to track AI model performance and detect anomalies. By integrating A/B testing and rollback mechanisms, they enable organizations to continuously improve AI applications while minimizing risks. Their ability to manage real-time updates is crucial in AI-driven businesses.

3. Security and Compliance Automation

Cybersecurity is a major concern in AI and deep learning applications. DevOps engineers integrate security into every stage of development, implementing DevSecOps practices. They use AI-driven security tools to detect vulnerabilities, automate patch management, and enforce compliance regulations.

By incorporating automated security scans and access control mechanisms, DevOps engineers reduce potential threats and data breaches. Their focus on security automation ensures that AI models operate in a protected environment while meeting industry standards like GDPR and HIPAA.

4. Observability and AI-Based Monitoring

DevOps engineers implement AI-powered observability tools to monitor system performance and detect anomalies. Advanced AIOps (Artificial Intelligence for IT Operations) solutions like Datadog, New Relic, and Prometheus allow them to predict potential failures before they occur.

By leveraging machine learning models, DevOps engineers enhance proactive troubleshooting and performance tuning. Their ability to analyze large volumes of data in real-time ensures seamless operations and minimizes downtime in AI-driven applications.

5. Managing Cloud-Native AI Workloads

With the rise of cloud computing, DevOps engineers are responsible for managing cloud-native AI workloads. They work with AWS, Google Cloud, and Azure to optimize machine learning workflows, ensuring cost-efficient resource utilization.

They implement serverless computing and autoscaling strategies to adapt to changing demands. Their expertise in cloud security, containerization, and orchestration ensures that AI applications remain highly available and scalable.

6. Data Pipeline and MLOps Integration

DevOps engineers collaborate with data engineers to build and manage AI-driven data pipelines. They ensure seamless integration of data processing frameworks like Apache Kafka and Apache Spark into production environments.

By adopting MLOps (Machine Learning Operations) best practices, they streamline the deployment and monitoring of AI models. Their expertise in automating ML workflows enhances model retraining and continuous learning.

7. Edge Computing and AI Model Deployment

Edge computing is transforming how AI models are deployed. DevOps engineers enable AI inferencing at the edge by optimizing model performance on edge devices. They use frameworks like TensorFlow Lite and ONNX Runtime to deploy AI models efficiently.

Their role includes managing edge-to-cloud synchronization, ensuring secure data transfer, and optimizing real-time AI analytics. This approach reduces latency and enhances AI-driven decision-making in industries like IoT, healthcare, and automotive.

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Salaries of DevOps Engineers in Different Countries & Regions

United States: ‍Glassdoor reports that DevOps engineers earn an estimated total salary of $142,314 per year, with an average base salary of $109,670 annually. The figure represents the median salary, calculated based on industry data and reported earnings from professionals in the field.

United Kingdom: According to DevIT, DevOps professionals in the UK typically earn an annual salary of around £57,500. The majority of salaries in this field range between £41,100 and £100,000 per year, depending on experience, skills, and industry demand.

India: In India, the salary of a DevOps Engineer typically falls between ₹3.8 Lakhs and ₹14.5 Lakhs per year for professionals with two to six years of experience. These figures are derived from the latest salary data collected from 93,900 DevOps Engineers across various industries.

Canada: A DevOps engineer earns an estimated total salary of CA$98,541 per year. The average base salary stands at approximately CA$ 91,910 annually, representing the midpoint of reported earnings in this field. DevOps engineers may receive an estimated pay of CA$ 6,631 per year.

Australia: A DevOps engineer can expect to earn a total annual compensation of approximately $125,000, with an average base salary of $120,000 per year. The figure represents the median salary, calculated from a range of reported earnings and industry data. In addition to the base salary, DevOps engineers may receive around $5,000 per year in extra compensation.

Certifications DevOps Engineers Should Attain to Remain Competitive

AWS Certified DevOps Engineer – Professional

The certification validates expertise in automating infrastructure, monitoring performance, and implementing security best practices within AWS environments. It is essential for DevOps engineers working with AI-driven cloud deployments and machine learning applications.

Google Professional DevOps Engineer

Designed for those leveraging Google Cloud, this certification ensures proficiency in CI/CD, site reliability engineering (SRE), and cloud automation. It is particularly valuable for DevOps engineers who are managing AI workloads and optimizing ML pipelines in Google Cloud.

Microsoft Certified: DevOps Engineer Expert

The certification focuses on Azure-based DevOps practices, including automation, CI/CD implementation, and security management. It is ideal for engineers working with AI-driven applications in Microsoft’s cloud ecosystem.

Certified Kubernetes Administrator (CKA)

Kubernetes is the backbone of container orchestration, and this certification demonstrates expertise in managing, deploying, and troubleshooting Kubernetes clusters. DevOps professionals must handle AI workloads in containerized environments.

HashiCorp Certified: Terraform Associate

The certification covers Infrastructure as Code (IaC) with Terraform, enabling engineers to automate cloud resource provisioning. It is essential for managing scalable AI and deep learning applications across multi-cloud environments.

Certified Jenkins Engineer (CJE)

Jenkins is a leading automation tool for CI/CD pipelines, and this certification ensures proficiency in implementing and maintaining Jenkins workflows. It is particularly useful for DevOps engineers who are integrating AI models into production environments.

Day-to-Day Activities and Deliverables List of DevOps Engineer

Automating Infrastructure Deployments and Scaling AI Workloads

DevOps engineers use Infrastructure as Code (IaC) tools like Terraform and AWS CloudFormation to automate cloud resource provisioning. This ensures AI applications can scale efficiently without manual intervention.

Managing CI/CD Pipelines for AI Model Integration

Continuous Integration and Continuous Deployment (CI/CD) pipelines streamline AI model updates, ensuring seamless transitions from development to production. DevOps engineers automate testing and deployment to maintain model accuracy and reliability.

Implementing Security and Compliance Automation

Security automation tools like AWS Security Hub and Google Cloud Security Command Center help enforce compliance standards. DevOps engineers integrate these solutions to protect AI applications from vulnerabilities and cyber threats.

Monitoring System Performance and Troubleshooting Issues

Using observability tools like Prometheus and Grafana, DevOps engineers track system health and AI workload efficiency. They proactively resolve performance bottlenecks to maintain seamless application operations.

Optimizing Cloud and Edge AI Applications

DevOps engineers fine-tune cloud resources and edge computing strategies to enhance AI model performance. They leverage serverless computing and GPU acceleration for faster processing and reduced latency.

Collaborating with Data Engineers and Software Developers

Close coordination between DevOps, data engineers, and software developers ensures AI models are efficiently deployed. This cross-functional teamwork enables smoother integration of machine learning algorithms into production.

Managing Containerized AI Workloads

By utilizing Docker and Kubernetes, DevOps engineers streamline the deployment and scaling of AI models. Containerization improves portability and ensures consistent performance across cloud and on-premise environments.

Key Departments DevOps Engineers Work With

1. Software Development

DevOps engineers collaborate with developers to integrate AI models into applications seamlessly. They ensure that deployment pipelines align with software release cycles, reducing downtime and increasing efficiency.

2. Data Science

They work closely with data scientists to deploy machine learning models in production. Their role involves managing data pipelines, optimizing AI workflows, and ensuring model scalability.

3. IT Security

Cybersecurity teams rely on DevOps engineers to implement security automation and enforce compliance. They work together to prevent vulnerabilities and protect sensitive AI-driven data.

4. Cloud Engineering

DevOps engineers coordinate with cloud architects to manage AI workloads efficiently. Their expertise in cloud security, autoscaling, and cost optimization is crucial for big AI applications.

5. Product Management

They assist product managers in aligning DevOps strategies with business goals. Their input ensures AI applications are deployed efficiently, meeting customer and market demands.

Roadmap to Become a DevOps Engineer

1. Gain proficiency in Linux, networking, and scripting languages.

2. Learn Infrastructure as Code (IaC) tools like Terraform and Ansible.

3. Master containerization and orchestration with Docker and Kubernetes.

4. Develop CI/CD automation skills using Jenkins and GitHub Actions.

5. Enhance cloud computing knowledge with AWS, Azure, or Google Cloud.

6. Learn security best practices and DevSecOps principles.

7. Obtain relevant certifications to validate expertise.

Conclusion

The role of a DevOps engineer has evolved significantly, especially with advancements in AI and deep learning. These professionals must stay ahead by mastering automation, cloud computing, and security best practices. Their expertise in managing scalable AI workloads ensures efficiency, reliability, and security.

By attaining industry-recognized certifications, they can enhance their career prospects and remain competitive. If you’re looking to enter this dynamic field, following the right roadmap will set you up for long-term success. Empower your team with Edstellar Corporate Training Courses to transform your profession.