We are looking for a Machine Learning Engineer with strong expertise in MLOps to join our team. This role focuses on developing and maintaining automated ML systems, from training pipelines to inference services deployment. The ideal candidate will have a solid understanding of CI/CD workflows, cloud environments, and modern ML tools.
Responsibilities:
Design, develop, and maintain MLOps systems to automate ML workflows
Create and optimize training pipelines for machine learning models
Implement and manage inference services for production environments
Collaborate with data scientists and software engineers to integrate ML solutions
Ensure best practices in ML model versioning, monitoring, and deployment
Requirements:
Proficiency in Python and familiarity with Data Science frameworks
Strong understanding of CI/CD principles and experience with Kubernetes
Familiarity with cloud platforms, particularly AWS
Experience with MLOps tools such as Kubeflow Pipelines, MLflow, and FastAPIKnowledge of big data processing frameworks like Apache Spark
Solid grasp of software engineering principles and best practices
Excellent problem-solving skills and ability to work in a collaborative environment
Preferred Qualifications:
Experience with containerization technologies and microservices architecture
Familiarity with data versioning and experiment tracking in ML projects
Knowledge of model serving technologies and scalable inference systems
We're looking for a candidate who can bridge the gap between machine learning development and production deployment, ensuring our ML systems are scalable, maintainable, and efficient."