
Mlops Engineer
4 days ago
Task Description:
• University Degree in Computer Science, Information Technology, or related field
• 5+ years of experience in the Machine Learning Operations role
• Design the data pipelines and engineering infrastructure to support our clients' enterprise machine learning systems at scale
• Take offline models data scientists build and turn them into a real machine learning production system
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients' machine learning systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Facilitate the development and deployment of proof-of-concept machine learning systems
• Communicate with clients to build requirements and track progress
• Strong analytic skills related to working with structured, semi structured and unstructured datasets
• Advanced Machine learning techniques: Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reductio.
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Senior MLOps Engineer
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Senior MLOps Engineer
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Senior MLOps Engineer
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India Mitchell Martin Inc. Full timeEssential Duties Include, but are not limited to, the following: Own productionizing models—from tracked experiments to governed releases—ensuring resilient services with clear SLOs, runbooks, and fast, safe rollbacks. Build automation-first delivery: reproducible builds, layered tests, and environment promotion via GitLab CI and Terraform-based IaC....
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India UPS Full time US$ 90,000 - US$ 1,20,000 per yearJob Description:Job Summary:Responsibilities:Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. ...
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