AWS DevOps Engineer with MLOps

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Job Overview

The AWS DevOps Engineer with MLOps at TieTalent will play a critical role in automating, managing, and optimizing our cloud infrastructure to support machine learning operations. This position entails implementing DevOps practices and tools to facilitate efficient data pipelines, model deployments, and monitoring. The successful candidate will collaborate with cross-functional teams to accelerate our machine learning initiatives while ensuring the stability and performance of our cloud environments.

Job Responsibilities

• Design, implement, and manage CI/CD pipelines to streamline machine learning model deployment.
• Automate infrastructure provisioning and management using Infrastructure as Code (IaC) practices.
• Monitor system performance and troubleshoot issues to ensure optimal operational capacity.
• Collaborate with data scientists and software engineers to enhance operational workflows and processes.
• Implement security best practices for cloud infrastructures and ML models.
• Document processes, configurations, and architectural decisions for transparency and knowledge sharing.
• Stay updated with the latest developments in AWS and MLOps tools to drive continuous improvement.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, or a related field.
• Proven experience in AWS cloud services, including EC2, S3, Lambda, and RDS.
• Strong understanding of DevOps practices and tools, such as Docker, Kubernetes, and Jenkins.
• Experience with MLOps frameworks and tools like MLflow, Kubeflow, or TFX.
• Proficient in scripting languages such as Python, Bash, or Ruby.
• Solid experience with monitoring tools like CloudWatch, Prometheus, or Grafana.
• Knowledge of database management and data warehousing solutions.
• Familiarity with Agile methodologies and collaborative software development processes.

Technologies & Tools

The AWS DevOps Engineer will utilize a range of advanced technologies and tools including, but not limited to, AWS (Amazon Web Services) for cloud management, Docker and Kubernetes for container orchestration, CI/CD tools such as Jenkins or GitLab, and monitoring solutions like CloudWatch and Grafana. Additional tools include MLOps platforms like MLflow, Kubeflow, and TensorFlow Extended (TFX), which are critical for scaling machine learning operations effectively.

Ideal Candidates

The ideal candidate for the AWS DevOps Engineer with MLOps position will possess a proactive mindset and a passion for continuous learning and improvement. They will demonstrate strong problem-solving skills and the ability to work collaboratively in a fast-paced environment. Excellent communication skills, both verbal and written, are essential for successful collaboration with technical and non-technical team members. A strong commitment to quality and an adaptable attitude will also contribute to the candidate's success within our innovative organization.