iLink Digital
ML Engineer
iLink Digital, Los Angeles, California, United States, 90079
Los Angeles, United States
| Posted on 08/13/2024iLink is a Global Software Solution Provider and Systems Integrator, delivering next-generation technology solutions to help clients solve complex business challenges, improve organizational effectiveness, increase business productivity, realize sustainable enterprise value and transform businesses inside-out. iLink integrates software systems and develops custom applications, components, and frameworks on the latest platforms for IT departments, commercial accounts, application services providers (ASP) and independent software vendors (ISV). iLink solutions are used in a broad range of industries and functions, including healthcare, telecom, government, oil and gas, education, and life sciences. iLink’s expertise includes Cloud Computing & Application Modernization, Data Management & Analytics, Enterprise Mobility, Portal, collaboration & Social Employee Engagement, Embedded Systems, and User Experience design.What makes iLink Systems' offerings unique is the fact that we use pre-created frameworks, designed to accelerate software development and implementation of business processes for our clients. iLink has over 60 frameworks (solution accelerators), both industry-specific and horizontal, that can be easily customized and enhanced to meet your current business challenges.Requirements
We are seeking a talented and experienced
Machine Learning Operations (ML Ops) Engineer
with 2-4 years of hands-on experience in deploying and managing machine learning models in a Microsoft Azure environment. As an ML Ops Engineer at iLink, you will play a crucial role in ensuring the scalability, reliability, and efficiency of our machine learning workflows and production systems.Responsibilities:Model Deployment and Management: Deploy machine learning models in Microsoft Azure using Azure Machine Learning Services and other relevant tools, ensuring smooth integration with our production systems.Automation and Orchestration: Develop and maintain automated pipelines and workflows for model training, deployment, and monitoring using tools such as Azure DevOps or other CI/CD platforms.Infrastructure Management: Provision, configure, and optimize Azure infrastructure components such as virtual machines, Kubernetes clusters, and data storage to support machine learning workloads.Monitoring and Logging: Implement robust monitoring and logging solutions to track the performance and health of deployed models and infrastructure. Set up alerts for proactive issue identification and resolution.Scalability and Performance: Collaborate with data scientists and engineers to optimize the performance of machine learning models, ensuring scalability to handle increasing data volumes and user loads.Security and Compliance: Enforce security best practices for machine learning models and data, ensuring compliance with industry standards and regulations.Version Control: Implement version control for machine learning models and associated code to track changes and facilitate collaboration among data science and engineering teams.Documentation: Maintain clear and up-to-date documentation for ML Ops processes, configurations, and best practices.Troubleshooting: Diagnose and resolve issues related to model deployment, infrastructure, and data pipelines on time.Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and data engineers, to ensure seamless integration of machine learning solutions into production systems.Qualifications:Bachelor's degree in computer science, engineering, or a related field. Master's degree is a plus.2-4 years of experience in ML Ops or related roles, with a strong background in deploying and managing machine learning models in a production environment.Experience with containerization technologies such as Docker and container orchestration using Kubernetes.Strong scripting and programming skills in languages like Python, and familiarity with machine learning libraries and frameworks.Knowledge of CI/CD pipelines and version control systems (e.g., Git).Excellent problem-solving and troubleshooting skills.Strong communication and collaboration skills to work effectively with cross-functional teams.
#J-18808-Ljbffr
| Posted on 08/13/2024iLink is a Global Software Solution Provider and Systems Integrator, delivering next-generation technology solutions to help clients solve complex business challenges, improve organizational effectiveness, increase business productivity, realize sustainable enterprise value and transform businesses inside-out. iLink integrates software systems and develops custom applications, components, and frameworks on the latest platforms for IT departments, commercial accounts, application services providers (ASP) and independent software vendors (ISV). iLink solutions are used in a broad range of industries and functions, including healthcare, telecom, government, oil and gas, education, and life sciences. iLink’s expertise includes Cloud Computing & Application Modernization, Data Management & Analytics, Enterprise Mobility, Portal, collaboration & Social Employee Engagement, Embedded Systems, and User Experience design.What makes iLink Systems' offerings unique is the fact that we use pre-created frameworks, designed to accelerate software development and implementation of business processes for our clients. iLink has over 60 frameworks (solution accelerators), both industry-specific and horizontal, that can be easily customized and enhanced to meet your current business challenges.Requirements
We are seeking a talented and experienced
Machine Learning Operations (ML Ops) Engineer
with 2-4 years of hands-on experience in deploying and managing machine learning models in a Microsoft Azure environment. As an ML Ops Engineer at iLink, you will play a crucial role in ensuring the scalability, reliability, and efficiency of our machine learning workflows and production systems.Responsibilities:Model Deployment and Management: Deploy machine learning models in Microsoft Azure using Azure Machine Learning Services and other relevant tools, ensuring smooth integration with our production systems.Automation and Orchestration: Develop and maintain automated pipelines and workflows for model training, deployment, and monitoring using tools such as Azure DevOps or other CI/CD platforms.Infrastructure Management: Provision, configure, and optimize Azure infrastructure components such as virtual machines, Kubernetes clusters, and data storage to support machine learning workloads.Monitoring and Logging: Implement robust monitoring and logging solutions to track the performance and health of deployed models and infrastructure. Set up alerts for proactive issue identification and resolution.Scalability and Performance: Collaborate with data scientists and engineers to optimize the performance of machine learning models, ensuring scalability to handle increasing data volumes and user loads.Security and Compliance: Enforce security best practices for machine learning models and data, ensuring compliance with industry standards and regulations.Version Control: Implement version control for machine learning models and associated code to track changes and facilitate collaboration among data science and engineering teams.Documentation: Maintain clear and up-to-date documentation for ML Ops processes, configurations, and best practices.Troubleshooting: Diagnose and resolve issues related to model deployment, infrastructure, and data pipelines on time.Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and data engineers, to ensure seamless integration of machine learning solutions into production systems.Qualifications:Bachelor's degree in computer science, engineering, or a related field. Master's degree is a plus.2-4 years of experience in ML Ops or related roles, with a strong background in deploying and managing machine learning models in a production environment.Experience with containerization technologies such as Docker and container orchestration using Kubernetes.Strong scripting and programming skills in languages like Python, and familiarity with machine learning libraries and frameworks.Knowledge of CI/CD pipelines and version control systems (e.g., Git).Excellent problem-solving and troubleshooting skills.Strong communication and collaboration skills to work effectively with cross-functional teams.
#J-18808-Ljbffr