CI/CD

MLOps Engineer

Job Title: MLops
Job Type: Full Time
Job Location: Dallas, TX

Job Description:

We are seeking a highly skilled and experienced Machine Learning Engineer with expertise in scaling and deploying complex ML models, preferably on Kubernetes, using open-source ML frameworks. The ideal candidate will have a strong background in machine learning, and DevOps/MLOps practices.

Key Responsibilities:

Develop, train, and deploy scalable machine learning models using frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Design and implement ML model deployment pipelines on Kubernetes clusters.
Optimize ML algorithms for performance and scalability.
Collaborate with data engineers to build and maintain efficient data pipelines for model training and inference.
Implement CI/CD pipelines for ML workflows to ensure seamless model deployment and updates.
Monitor and maintain the health, performance, and reliability of deployed ML models.
Work closely with cross-functional teams to integrate ML models into production applications.
Stay up-to-date with the latest developments in machine learning, Kubernetes, and open-source technologies.

Qualifications:
8+ years of experience in machine learning.
Prior experience in a production environment with large-scale ML deployments
Proven experience with Kubernetes and container orchestration.’
Strong proficiency in open-source ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Experience with CI/CD tools and practices, particularly for ML workflows.
Proficiency in programming languages such as Python, and familiarity with libraries and tools for data manipulation and analysis.
Familiarity with cloud platforms such as AWS, GCP, or Azure.
Excellent problem-solving skills and the ability to work in a fast-paced, dynamic environment.

Education: Bachelor’s Degree Required and Masters Preferred

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Principal Analyst – MLOps Engineer

Role Overview
We are seeking a highly skilled Senior MLOps Engineer with 8+ years of experience to join our team. The ideal candidate will have extensive expertise in model deployment, model monitoring, and productionizing machine learning models. You will play a crucial role in designing and implementing efficient workflows for AI programming and team communication, ensuring seamless integration of ML solutions within our organization.
Key Responsibilities:
• Workflow Design & Implementation: Oversee the implementation of workflows for AI programming and team communication, ensuring optimal collaboration and efficiency.
• Model Deployment: Manage and optimize model deployment processes, including the use of Kubernetes for containerized model deployment and orchestration.
• Model Registry Management: Maintain and manage a model registry to track versions and ensure smooth transitions from development to production.
• CI/CD Implementation: Develop and implement Continuous Integration/Continuous Deployment (CI/CD) pipelines for model training, testing, and deployment, ensuring high code quality through rigorous model code reviews.
• Model Monitoring & Optimization: Design and implement model inference pipelines and monitoring frameworks to support thousands of models across various pods, optimizing execution times and resource usage.
• Team Leadership & Training: Manage, mentor, and train junior engineers, fostering their growth and learning while overseeing a large team
• Collaboration with Data Science Teams: Train and collaborate with data science team members on best practices in tools such as Kubeflow, Jenkins, Docker, and Kubernetes to ensure smooth model productionization.
• Reusable Frameworks Development: Draft designs and apply reusable frameworks for drift detection, live inference, and API integration.
• Cost Optimization Initiatives: Propose and implement strategies to reduce operational costs, including optimizing models for resource efficiency, resulting in significant annual savings.
• Documentation & Standards Development: Produce MLE standards documents to assist data science teams in deploying their models effectively and consistently.
Qualifications:
• 8+ years of experience in MLOps, model deployment, and productionizing machine learning models.
• Proficient in Kubernetes, model monitoring, and CI/CD practices. Experience working in the Azure environment.
• Strong understanding of model registry concepts and best practices.
• Experience with programming languages and ML frameworks (e.g., TensorFlow, PyTorch).
• Proven track record of optimizing ML workflows and processes.
• Excellent communication and leadership skills, with experience in mentoring and training team members.
• Ability to work in a fast-paced, collaborative environment.

Principal Analyst – MLOps Engineer Read More »

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