Senior Machine Learning Engineer

Ericsson; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Ericsson Enterprise Wireless Solutions seeks an experienced ML/DevOps engineer to design, build, and maintain end-to-end ML pipelines and deploy models as scalable microservices. You will automate infrastructure with Terraform and CloudFormation, and integrate ML workloads with AWS services including SageMaker, Lambda and EKS.

You will collaborate with DevOps and platform teams to improve observability, cost efficiency, and monitoring for data drift and model performance, while upholding strong

Qualifications

• 5–8 years of experience in ML/Software/DevOps roles focusing on ML systems.
• Proficient Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Strong AWS experience (SageMaker, Lambda, EKS/ECS, CloudWatch, S3, IAM).

Responsibilities

• Design, build, and maintain end-to-end ML pipelines for data ingestion, model training, evaluation, deployment and monitoring.
• Deploy ML models as scalable microservices/APIs with high availability and low latency.
• Automate infrastructure provisioning using Terraform, 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 (SageMaker, EKS, Lambda).
• Work with DevOps and platform teams to improve observability and cost efficiency.
• Implement monitoring 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.

Skills

ML pipelines

Python

TensorFlow

PyTorch

Scikit-learn

CI/CD

DevOps

Observability

Model monitoring

Code reviews

Education

Bachelor’s or Master’s in CS/Engineering

Tools

Terraform

AWS

Docker

Kubernetes

SageMaker

FastAPI / Flask

gRPC

Git

Argo Workflows

Job description

Join our Team

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 accredited institutions and are further subject to verification