Senior Machine Learning Engineer
4 weeks ago
About the Role:
We are looking for a skilled Machine Learning Operations (MLOps) professional to lead the development and deployment of machine learning models. The successful candidate will be responsible for streamlining the process of deploying and maintaining machine learning models and overseeing the unification of machine learning system development and deployment and operations.
Key Responsibilities:
- 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 MLOps.
- 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 cloud-based data engineering solutions.
- Collaborate with cross-functional teams to deliver data engineering solutions that meet business needs.
- Ensure that the machine learning models are deploying and operating efficiently and effectively.
- Develop and implement metrics to measure the performance of the machine learning models.
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 practices (CI/CD) in software development life cycles (SDLC).
- Good programming knowledge, hand-on experience with ML frameworks, libraries, agile environments, and deploying machine learning solutions using DevOps principles.
- Knowledge of data structures, data modeling, 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.
- 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 to deliver data engineering solutions that meet business needs.
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