Designs and maintains end-to-end machine learning pipelines, deploys models as scalable microservices, and manages production ML infrastructure on AWS. Responsibilities include CI/CD automation, infrastructure provisioning, model monitoring, observability, data-drift detection, API and SDK development, testing, documentation, and collaboration with DevOps and platform teams. The role also supports cloud-native architectures, model versioning, rollback, and efficient distributed model serving.
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About this opportunity:
Ericsson Enterprise Wireless Solutions (BEWS) drives Ericsson's Enterprise Networking and Security business. Our portfolio covers wide area networks, local area networks, enterprise security, Private 5G networks and Secure Access Service Edge (SASE) solutions.
What Will You Do:
• Design, build and maintain end-to-end ML pipelines for data ingestion, model training, evaluation, deployment and monitoring.• Deploy ML models as scalable microservices and APIs with high availability and low latency.• Automate infrastructure provisioning using Terraform, AWS CloudFormation or similar tools.• Build and manage CI/CD pipelines for model deployment, versioning and rollback.• Integrate ML models into cloud-native architectures using AWS services such as SageMaker, EKS, Lambda, S3, ECS and CloudWatch.• Work with DevOps and platform teams to improve consistency, observability and cost efficiency.• Implement monitoring and alerting for model performance, data drift and system health.• Develop reusable microservices, APIs and SDKs for ML model integration.• Maintain strong standards for code quality, testing and documentation.• Stay updated on developments in MLOps, cloud computing and distributed model serving.
What Will You Bring:
• 5-8 years of experience in ML Engineering, Software Engineering or DevOps, with a focus on machine learning systems.• Strong Python skills and experience with TensorFlow, PyTorch or Scikit-learn.• Strong experience with AWS services including SageMaker, Lambda, ECS/EKS, CloudWatch, S3 and IAM.• Experience deploying ML models into production using FastAPI, Flask, gRPC or similar frameworks.• Hands-on experience with Terraform and Infrastructure as Code.• Experience with Docker and Kubernetes.• Good understanding of CI/CD pipelines, model registries and artifact management.• Knowledge of model monitoring, logging and observability tools such as Prometheus, Grafana or Evidently AI.• Strong software engineering practices including version control, modular design, automated testing and code reviews.
Preferred Qualifications:
• Bachelor's or Master's degree in Computer Science, Engineering or a related field.• Experience with AWS CDK, Serverless Framework or Argo Workflows.• Familiarity with API gateways, Kafka, SQS and event-driven systems.• Exposure to A/B testing, canary and shadow deployments for ML models.• Experience in AIOps, telecom or enterprise networking is an advantage.
All academic credentials must be from recognized and a cc redited institutions and are further subject to verification
About this opportunity:
Ericsson Enterprise Wireless Solutions (BEWS) drives Ericsson's Enterprise Networking and Security business. Our portfolio covers wide area networks, local area networks, enterprise security, Private 5G networks and Secure Access Service Edge (SASE) solutions.
What Will You Do:
• Design, build and maintain end-to-end ML pipelines for data ingestion, model training, evaluation, deployment and monitoring.• Deploy ML models as scalable microservices and APIs with high availability and low latency.• Automate infrastructure provisioning using Terraform, AWS CloudFormation or similar tools.• Build and manage CI/CD pipelines for model deployment, versioning and rollback.• Integrate ML models into cloud-native architectures using AWS services such as SageMaker, EKS, Lambda, S3, ECS and CloudWatch.• Work with DevOps and platform teams to improve consistency, observability and cost efficiency.• Implement monitoring and alerting for model performance, data drift and system health.• Develop reusable microservices, APIs and SDKs for ML model integration.• Maintain strong standards for code quality, testing and documentation.• Stay updated on developments in MLOps, cloud computing and distributed model serving.
What Will You Bring:
• 5-8 years of experience in ML Engineering, Software Engineering or DevOps, with a focus on machine learning systems.• Strong Python skills and experience with TensorFlow, PyTorch or Scikit-learn.• Strong experience with AWS services including SageMaker, Lambda, ECS/EKS, CloudWatch, S3 and IAM.• Experience deploying ML models into production using FastAPI, Flask, gRPC or similar frameworks.• Hands-on experience with Terraform and Infrastructure as Code.• Experience with Docker and Kubernetes.• Good understanding of CI/CD pipelines, model registries and artifact management.• Knowledge of model monitoring, logging and observability tools such as Prometheus, Grafana or Evidently AI.• Strong software engineering practices including version control, modular design, automated testing and code reviews.
Preferred Qualifications:
• Bachelor's or Master's degree in Computer Science, Engineering or a related field.• Experience with AWS CDK, Serverless Framework or Argo Workflows.• Familiarity with API gateways, Kafka, SQS and event-driven systems.• Exposure to A/B testing, canary and shadow deployments for ML models.• Experience in AIOps, telecom or enterprise networking is an advantage.
All academic credentials must be from recognized and a cc redited institutions and are further subject to verification
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