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Position Description

As a (Senior) MLOps Engineer, you will play a crucial role in building and maintaining the infrastructure and processes required to support machine learning operations. You will be responsible for preparing datasets, evaluating machine learning models using key performance indicators (KPIs), validating and deploying models, and ensuring their seamless integration into production systems (embedded and Cloud). Additionally, you will design and implement CI/CD pipelines for machine learning, automate ML operations, and utilize cloud-based solutions, such as AWS with Terraform, to enhance scalability and efficiency. This position requires a proactive individual with a strong foundation in MLOps practices, cloud platforms, and automation tools.

In this role, you will:

• Prepare and analyze datasets to support machine learning model development and training
• Assess natural language processing and computer vision models using relevant KPIs and metrics
• Validate models to ensure they meet performance standards and align with project requirements
• Deploy machine learning models into production environments with scalability and reliability
• Continuously monitor deployed models to ensure optimal performance and address issues as they arise
• Design, develop, and maintain CI/CD pipelines to streamline ML model development and deployment workflows
• Automate repetitive and manual processes involved in machine learning operations to improve efficiency
• Implement and manage MLOps solutions on AWS, leveraging Terraform for infrastructure as code

What you will need to succeed:

• 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 experience in CI/CD tools (e.g., Jenkins)
• Proficiency in automation tools for streamlining ML operation
• 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