SO MLOps Integration Engineer, GMET
Core
Design, develop, and maintain robust MLOps pipelines for model training, deployment, monitoring, and retraining within enterprise systems.
Role type
Senior MLOps Integration Engineer
Builds
Scalable machine learning workloads and integrated ML models into applications
Domain
Financial services / Machine Learning Operations
Deliverable
production ML models
Required skills
Python, MLOps platforms (MLflow, Kubeflow, Sagemaker, Azure ML, Vertex AI), containerization (Docker, Kubernetes), cloud platforms (AWS, Azure, GCP), CI/CD tools (Jenkins, GitLab CI, Azure DevOps), data engineering (SQL, Spark, Kafka)
Preferred skills
Financial services industry experience
Technologies
Docker, Kubernetes, AWS, Azure, GCP, MLflow, Kubeflow, Sagemaker, Azure ML, Vertex AI, Jenkins, GitLab CI, Azure DevOps, SQL, Spark, Kafka
Responsibilities
Design and maintain MLOps pipelines; Implement infrastructure for scalable ML workloads; Collaborate with data scientists and IT teams; Develop automated testing and CI/CD strategies; Establish monitoring and alerting systems; Troubleshoot ML deployment issues; Ensure compliance with security policies; Research new MLOps tools; Document processes and architectures