Sr. MLOps Engineer With DevOps Experience

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Job Overview

The Senior MLOps Engineer with DevOps Experience at E-Solutions will play a crucial role in bridging the gap between machine learning and operational workflows. The selected candidate will be responsible for developing and maintaining the ML lifecycle, implementing automation for deployment processes, and collaborating with cross-functional teams to ensure the smooth integration of machine learning models into production. This position will require a strong foundation in both MLOps and DevOps practices, enabling the engineer to facilitate the deployment, monitoring, and management of ML applications in a scalable and efficient manner.

Job Responsibilities

• Design, implement, and manage MLOps pipelines for machine learning model deployment and monitoring.
• Collaborate with data scientists and machine learning engineers to ensure smooth transition from model development to production.
• Automate deployment processes using CI/CD pipelines and configuration management tools.
• Monitor and troubleshoot production ML applications for performance, latency, and reliability issues.
• Ensure compliance with best practices in operational excellence, security, and data privacy.
• Document processes, workflows, and architecture to facilitate knowledge sharing and onboarding.
• Participate in architecture design sessions and contribute to the strategy for scalable ML infrastructure.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in DevOps, with a strong focus on MLOps practices.
• Proficiency in CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
• Hands-on experience with container orchestration technologies like Kubernetes and Docker.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong programming skills in languages such as Python, R, or Java.
• Experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.

Technologies & Tools

The Senior MLOps Engineer will utilize a range of technologies and tools to enhance machine learning operations. Familiarity with tools such as Kubernetes for container orchestration, Docker for environment management, CI/CD tools like Jenkins and Git, as well as monitoring tools such as Prometheus or Grafana, will be essential. Knowledge of cloud services such as AWS Sagemaker or Azure ML will also be advantageous to effectively deploy and manage machine learning models in a cloud environment.

Ideal Candidates

The ideal candidates for this position will possess a blend of technical expertise and strong interpersonal skills. They should be proactive problem solvers with a detail-oriented mindset, able to work collaboratively across diverse teams. Excellent communication skills are vital to articulate complex concepts simply and to drive projects to completion efficiently. A passion for continuous learning and staying up-to-date with the latest advancements in MLOps and DevOps practices will further distinguish outstanding candidates.