Machine Learning (ML) Ops Engineer
Fully remotePart-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- AWSDockerPythonGCPGitJavaKubeflowKubernetesMLFlowAirflowAzureGrafanaPrometheusScala
Requirements
- Proven experience as an MLOps Engineer or similar role
- Fintech or AI startup experience is a plus
- Strong programming skills in Python
- Familiarity with Java or Scala is a plus
- Experience with ML frameworks and pipeline orchestration (MLflow, Airflow, Kubeflow, or SageMaker)
- Hands-on experience with cloud services (AWS, Azure, or GCP)
- Solid knowledge of containerization and orchestration (Docker, Kubernetes)
- Familiarity with Git and CI/CD tools
- Strong understanding of ML algorithms and their real-world applications
- Excellent communication and collaboration skills in a remote, cross-timezone team environment
- Bachelors or Masters degree in Computer Science, Data Science, or related field (or equivalent experience)
Responsibilities
- Design, build, and maintain end-to-end machine learning infrastructure ensuring scalability, high availability, and performance
- Automate ML pipelines with Docker, Kubernetes, and CI/CD frameworks
- Collaborate with data scientists and engineers to streamline model deployment
- Monitor and optimize models in production with observability tools (e.g., Prometheus, Grafana, ELK)
- Manage and optimize cloud infrastructure (AWS, Azure, or GCP) for efficiency and cost
- Ensure compliance and security protocols for sensitive financial data
- Provide support and maintenance to ensure the reliability of ML applications
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