We are seeking an MLOps Engineer who has a deep passion for machine learning, operations, and technology, combined with a strong drive to implement scalable and efficient solutions.
We foster a professional environment where experienced engineers collaborate and contribute to the team’s success while continuously enhancing their own skills.
As an MLOps Engineer, you will have the unique opportunity to play a pivotal role in building and maintaining our machine learning infrastructure and operations.
We value MLOps engineers with a diverse skill set, a comprehensive background in both machine learning and DevOps, and a genuine interest in optimizing the deployment, monitoring, and maintenance of ML models beyond standard practices.
Role:
– Design, implement, and maintain scalable ML infrastructure for model training, deployment, and monitoring
– Ownership of the technical architecture for new ML operations features and enhancements
– Lead the development and optimization of ML pipelines, ensuring robust and efficient workflows
– Stay updated and lead technological advances in ML operations and infrastructure
Collaborate with data scientists and engineers to integrate ML models into production systems
We foster a professional environment where experienced engineers collaborate and contribute to the team’s success while continuously enhancing their own skills.
As an MLOps Engineer, you will have the unique opportunity to play a pivotal role in building and maintaining our machine learning infrastructure and operations.
We value MLOps engineers with a diverse skill set, a comprehensive background in both machine learning and DevOps, and a genuine interest in optimizing the deployment, monitoring, and maintenance of ML models beyond standard practices.
Role:
– Design, implement, and maintain scalable ML infrastructure for model training, deployment, and monitoring
– Ownership of the technical architecture for new ML operations features and enhancements
– Lead the development and optimization of ML pipelines, ensuring robust and efficient workflows
– Stay updated and lead technological advances in ML operations and infrastructure
Collaborate with data scientists and engineers to integrate ML models into production systems
Requirements:
At least 4 years of solid experience in Backend Engineering
At least 3 years of experience with cloud platforms like AWS, GCP, or Azure
Minimum 2 years of solid experience with containerization and orchestration tools like Docker and Kubernetes
Excellent verbal and written communication skills in English
A degree in Computer Science, Data Science, or a related discipline, or relevant industry experience
Self-taught practitioners are always welcome
At least 2 years of experience with continuous integration/continuous deployment (CI/CD) tools and practices
Hands-on experience building and managing data pipelines and workflows
Familiarity with SQL & NoSQL databases (e.g., MySQL, Redis)
Experience in Python advantage
Experience with ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn
At least 4 years of solid experience in Backend Engineering
At least 3 years of experience with cloud platforms like AWS, GCP, or Azure
Minimum 2 years of solid experience with containerization and orchestration tools like Docker and Kubernetes
Excellent verbal and written communication skills in English
A degree in Computer Science, Data Science, or a related discipline, or relevant industry experience
Self-taught practitioners are always welcome
At least 2 years of experience with continuous integration/continuous deployment (CI/CD) tools and practices
Hands-on experience building and managing data pipelines and workflows
Familiarity with SQL & NoSQL databases (e.g., MySQL, Redis)
Experience in Python advantage
Experience with ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn
This position is open to all candidates.
