Consultant / Sr.Consultant - DevOps MLOps Engineer

As a Consultant / Sr. Consultant - DevOps MLOps Engineer at Fresh Gravity, your main responsibilities include implementing CI/CD pipelines, managing infrastructure with IaC tools, deploying machine learning models, and ensuring collaboration across development, QA, and operations teams. You will also focus on performance optimization, security practices in deployments, and data management.

To become a Consultant / Sr. Consultant - DevOps MLOps Engineer at Fresh Gravity, you should possess strong experience with DevOps tools, knowledge of CI/CD processes, and familiarity with cloud platforms like AWS and Azure. Proficiency in programming languages such as Python and experience with monitoring tools like Prometheus are also beneficial.

At Fresh Gravity, we prioritize your growth through our 5Dimensions benefits program. This offers opportunities for enriching your skills, enhancing client relationships, and supporting personal development. We are committed to providing an environment where you can excel and exceed expectations.

As a Consultant / Sr. Consultant - DevOps MLOps Engineer at Fresh Gravity, familiarity with tools such as AWS, Azure, Docker, Kubernetes, Jenkins, and GitLab is essential. You should also be comfortable with IaC tools like Terraform, configuration management through Ansible, and monitoring tools for system reliability.

DevSecOps is integral to the role of a Consultant / Sr. Consultant - DevOps MLOps Engineer at Fresh Gravity. It involves integrating security practices into the CI/CD pipelines, ensuring that software deployments meet both security and regulatory standards, which is vital for the integrity of our applications.

CI/CD stands for Continuous Integration and Continuous Deployment. In this role, you would set up automated pipelines that enable developers to integrate their code changes frequently and deploy new features swiftly. Your approach should emphasize reliability and streamline processes to ensure rapid delivery.

To foster collaboration, communication is essential. Organizing regular syncs and using tools like Slack or Jira can help keep all teams informed and aligned with the project goals, ensuring that everyone is on the same page throughout the development lifecycle.

Infrastructure as Code (IaC) allows you to manage and provision infrastructure through code, enabling consistent and repeatable deployments. In this role, you would typically use tools like Terraform or Ansible to declare the desired state of your infrastructure, ensuring smooth scalability and management.

I have extensive experience with monitoring systems such as Prometheus, Grafana, and AWS CloudWatch. Monitoring application metrics, setting alerts, and conducting regular log analysis are crucial practices to ensure system reliability and quick troubleshooting.

Automated model deployment involves creating a robust pipeline that seamlessly integrates the training, validation, and deployment processes. Leveraging tools like Azure ML and MLflow can help manage model lifecycles effectively, ensuring that models can be quickly updated and monitored in production.

Best practices for managing security in DevOps include integrating security requirements early in the development process, employing automated testing for security vulnerabilities, and regularly updating dependencies and environments to mitigate risks effectively.

I have hands-on experience with major AWS services like S3, ECS, and SageMaker, as well as Azure services such as Azure DevOps and Azure Machine Learning. This encompasses managing deployments, optimizing costs, and ensuring scalability across different projects.

Performance optimization is essential in my daily role, as it ensures that applications run efficiently. I focus on fine-tuning application and infrastructure configurations and use monitoring tools to identify bottlenecks that may affect performance.

To stay updated, I follow industry leaders on social media, participate in webinars, and regularly read relevant blogs and publications. Engaging in community discussions and attending conferences also helps me learn about emerging trends and best practices.

Effective data management involves ensuring data quality, proper metadata handling, and implementing robust data pipelines. I prioritize clean datasets for training and have systems in place for data governance to ensure compliance and availability.