ML Ops Engineer

2 weeks ago

Bucharest, Bucureşti, Romania Data Edge Full time

Contract Type: Freelance SRL or PFA/Remote
Contracting period: 6 months
Role Overview
We are looking for a skilled
MLOps Engineer
to join our data and machine learning initiatives. In this role, you will be responsible for deploying, operating, and scaling machine learning models in production environments, ensuring reliability, performance, and seamless integration with cloud-based data platforms.

You will work closely with data scientists, data engineers, and platform teams to bridge the gap between model development and production operations.

Key Responsibilities

  • Deploy, monitor, and maintain machine learning models in production environments
  • Design and implement MLOps pipelines for model training, validation, deployment, and retraining
  • Collaborate with data science teams to operationalize TensorFlow-based models
  • Build and maintain cloud-native ML infrastructure
  • Implement monitoring for model performance, data drift, and system health
  • Manage versioning for models, data, and pipelines
  • Ensure scalability, reliability, and security of ML systems
  • Automate workflows using CI/CD pipelines and Infrastructure-as-Code
  • Optimize ML pipelines for performance and cost efficiency

Required Skills & Experience

  • Strong experience with Machine Learning and MLOps practices
  • Hands-on experience with TensorFlow in production environments
  • Strong proficiency in Python for ML and automation
  • Solid experience working with data platforms and SQL for data analysis and transformations
  • Experience with cloud platforms (AWS, GCP, or Azure)
  • Understanding of ML lifecycle management, from experimentation to production

Nice-to-Have Skills

  • Experience with model monitoring and observability tools
  • Familiarity with Docker and Kubernetes
  • Experience with feature stores and data versioning tools
  • Exposure to big data technologies or streaming systems
  • Experience with DevOps / CI/CD practices for ML workloads

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