Senior Machine Learning Engineer Bangalore,Karnataka,India Product Development

Ericsson; Bengaluru; In loco; INR 2.500.000 - 4.000.000; Tempo pieno

Descrizione del lavoro

Ericsson Bangalore is seeking a Senior Machine Learning Engineer to design end-to-end ML pipelines, train models and deploy scalable microservices in a cloud-native environment. You will work with AWS SageMaker, Terraform, Docker and Kubernetes, build CI/CD for ML workflows, monitor model performance and ensure observability and cost efficiency.

Candidates should have 5–8 years of relevant experience, strong Python, and experience with ML frameworks such as TensorFlow or PyTorch, plus knowledge

Competenze

• 5–8 years in ML/Software/DevOps with ML systems focus.
• Strong Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience with AWS services (SageMaker, Lambda, ECS/EKS, S3, CloudWatch).

Mansioni

• Design, build and maintain end-to-end ML pipelines from data ingestion to deployment.
• Deploy ML models as scalable microservices and APIs with high availability.
• Develop infrastructure as code with Terraform and manage CI/CD for ML workflows.
• Collaborate with DevOps and platform teams to improve observability and cost efficiency.

Conoscenze

Python

ML Engineering

DevOps

TensorFlow

PyTorch

Scikit-learn

AWS SageMaker

AWS Lambda

ECS/EKS

CloudWatch

S3

IAM

FastAPI/Flask

gRPC

Terraform

CI/CD

Docker

Kubernetes

Prometheus

Grafana

Evidently AI

Formazione

Bachelor’s or Master’s degree in Computer Science/Engineering or related

Strumenti

Docker

Kubernetes

FastAPI

Flask

gRPC

Terraform

SageMaker

CloudWatch

EKS

Lambda

S3

Prometheus

Grafana

Descrizione del lavoro

## Senior Machine Learning EngineerBangalore,Karnataka,IndiaFind out how well you match with this jobJob ID: 792933## 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**