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DevOps Engineer

2 months ago

Hyderabad, India Prudent Technologies and Consulting, Inc. Full time

DevOps and MLOps Engineer


We are seeking an experienced and highly skilled DevOps and MLOps Engineer to join our team. The ideal candidate will have a deep understanding of DevOps practices, cloud infrastructure, and the deployment of machine learning models into production environments. This role will be responsible for designing, implementing, and maintaining the infrastructure and automation required to support the end-to-end lifecycle of machine learning models.


Responsibilities:


  1. Architect, develop, and maintain the ML platform to support training and inference of ML models.
  2. Collaborate with data scientists and software engineers to define requirements and ensure seamless integration of ML workflows into the platform.
  3. Optimize the platform's performance and scalability, considering factors such as GPU resource utilization, data ingestion, model training, and deployment.
  4. Monitor and troubleshoot system performance, identifying and resolving issues to ensure the availability and reliability of the ML platform.
  5. Implement and maintain CI/CD pipelines for automated model training, evaluation, and deployment using technologies like ArgoCD and Argo Workflow.
  6. Implement and maintain a monitoring stack using Prometheus and Grafana to ensure the health and performance of the ML platform.
  7. Design and implement CI/CD pipelines for machine learning models, ensuring seamless integration, testing, and deployment into production environments.
  8. Build and maintain Kubernetes-based infrastructure, including extensions, custom resource definitions (CRDs), and operators using frameworks like the Operator SDK.
  9. Develop and maintain real-time, large-scale, and production-grade microservices applications, ensuring high availability, disaster recovery, and efficient deployment strategies.
  10. Implement and automate model and data versioning, version control, and tracking of model versions, training data, and hyperparameters.
  11. Develop and maintain automated processes for model output and data validation, incorporating relevant metrics for each machine learning algorithm.
  12. Implement monitoring and alerting systems for build and production environments, ensuring reliability and efficient maintenance of ML systems.
  13. Collaborate with data engineers, machine learning engineers, and software developers to streamline the integration of machine learning models into production systems.
  14. Automate infrastructure provisioning and management using tools like Terraform, Ansible, and Jenkins.
  15. Ensure compliance with security best practices, access controls, and data privacy regulations.
  16. Stay up-to-date with the latest advancements in MLOps, distributed computing, GPU acceleration technologies, and proactively propose improvements to enhance the ML platform.
  17. Provide technical guidance and mentorship to junior team members.

Skills and Experience:


  • 6+ years of experience in DevOps and MLOps roles, with a focus on deploying machine learning models into production environments.
  • Deep understanding of Kubernetes internal architecture, service mesh, container networking interfaces (CNI), container storage interfaces (CSI), and operators.
  • Hands-on experience with Kubernetes extensions, custom resource definitions (CRDs), and operators using frameworks like the Operator SDK.
  • Proven experience in designing and building real-time, large-scale, and production-grade microservices applications.
  • Extensive experience with production implementation of SaaS and Kubernetes-based applications, including high availability, disaster recovery, and deployment strategies.
  • Proficiency in scripting and automation tools, such as Bash, Terraform, Ansible, Jenkins, and Chef.
  • Solid understanding of container technologies, including Docker, Kubernetes, and related tools (e.g., flannel, CoreOS).
  • Experience working with multiple cloud providers (AWS, GCP, Azure) and a good knowledge of full-stack infrastructure.
  • Familiarity with build practices in MLOps that combine machine learning, DevOps, and data engineering principles.
  • Understanding of model and data versioning, automated version control, and tracking of model versions and training data.
  • Experience with model output and data validation, including metrics relevant to machine learning algorithms.
  • Knowledge of monitoring tools and practices for build and production systems, including automated monitoring and alerting.
  • Familiarity with distributed computing and scalable infrastructure solutions for machine learning platforms.
  • Experience with CI/CD tools like ArgoCD and Argo Workflow for automated model training, evaluation, and deployment.
  • Knowledge of monitoring stacks like Prometheus and Grafana for monitoring the health and performance of ML platforms.

Education and Certifications:


  • Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
  • Relevant certifications in cloud platforms (e.g., AWS Certified DevOps Engineer, Google Cloud Certified Professional Cloud DevOps Engineer, Microsoft Certified: Azure DevOps Engineer Expert) are added advantage.