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.
Requirements:
All about You/Experience:
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.
All about You/Experience:
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.
