Requirements :
* Several years of proven experience in MLOps, including end-to-end machine learning lifecycle management
* Strong programming skills in Python and C++
* Familiarity with MLOps tools like MLFlow, Airflow, or Kubeflow
* Experience designing and managing CI / CD pipelines for machine learning projects with CI / CD tools (e.g., Jenkins)
* Proficiency in automation tools for streamlining ML operations
* Experience in Natural Language Processing (NLP) and Computer Vision (CV), workflows, and metrics
* Hands-on experience in model validation, testing, and deployment to production
* Strong verbal and written communication skills in English
Responsibilities :
* As a (Senior) MLOps Engineer, you will build and maintain infrastructure and processes for machine learning operations. Responsibilities include preparing datasets, evaluating models with KPIs, validating, deploying models, and ensuring seamless integration into production (embedded and cloud). You will also design and implement CI / CD pipelines, automate ML operations, and utilize cloud solutions like AWS with Terraform to enhance scalability and efficiency. This role requires a proactive individual with a solid foundation in MLOps, cloud platforms, and automation tools.
* Prepare and analyze datasets for ML model development and training
* Assess NLP and CV models using relevant KPIs and metrics
* Validate models to meet performance standards and project requirements
* Deploy models into production environments ensuring scalability and reliability
* Monitor deployed models to maintain performance and address issues
* Design, develop, and maintain CI / CD pipelines for ML workflows
* Automate repetitive ML operations to improve efficiency
* Implement and manage MLOps solutions on AWS, leveraging Terraform for infrastructure as code
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