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Machine Learning Operations Lead
2 months ago
Summary :
We are seeking a skilled Machine Learning Operations, or MLOps Lead who would be responsible for streamlining the process of deploying and maintaining machine learning (ML) models and oversee unification of ML system development (Dev) and ML system deployment and operations (Ops).
As MLOps is critical to systematically and simultaneously manage the release of new ML models with application code and data changes, the ML Ops lead is required to automate and standardize processes across the ML lifecycle, including model development, testing, integration, release, and infrastructure :
- Design, Implement, and Maintain machine learning infrastructure, pipelines, and workflows.
- Develop and maintain a platform that automates creating, training, deploying, and updating machine learning models.
- Work with the data science team to ensure that the algorithms are performing as expected.
- Build new models and improve existing ones using established tools and platforms for ML Ops.
- Understand business problems and come up with solutions to them using machine learning techniques.
- Create pipelines for data to flow through, train models, and set up applications
Requirements :
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
- Proven experience as a MLOps Engineer or similar role.
- Strong knowledge of continuous delivery practice (CI/CD) of DevOps in SDLC
- Good programming knowledge, hands-on experience with ML frameworks, libraries, agile environments, and deploying machine learning solutions using DevOps principles.
- Knowledge of data structures, data modelling, and database management systems.
- Understanding of the framework/environments led by QA.
- Knowledge of tools in the pipeline that serve different purposes, such as Continuous Integration servers, Configuration management, Deployment automation, Containers, Infrastructure Orchestration, Monitoring and Analytics, Testing and Cloud Quality tools, and network protocols.
- Ability to automate the entire DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations.
- Experience with model validation, model training, and other aspects of evaluating an ML system
- Familiarity with Agile development methodologies.
- Excellent written and verbal communication skills.
- Strong problem-solving ability.
- Ability to work collaboratively with cross-functional teams, including Data Scientists, Software Engineers, and Business stakeholders, to deliver data engineering solutions that meet business needs.
- Strong attention to detail and ability to manage multiple projects simultaneously.
