{"id":3144595,"url":"https://alion.io/job/ericsson-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":56456,"name":"Ericsson","domain":"ericsson.com","url":"https://alion.io/company/ericsson","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":83,"open_postings":113,"ghost_share":0.009,"stale_share":0.637,"repost_share":0.009,"time_to_fill_p50_days":33,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Docker","optional":false},{"name":"Evidently AI","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Grafana","optional":false},{"name":"gRPC","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"A/B Testing","optional":true},{"name":"AIOps","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Argo Workflows","optional":true},{"name":"AWS CDK","optional":true}],"status":"live","first_seen_at":"2026-10-15T12:58:24Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-27T14:21:45Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"Join our Team\nAbout this opportunity:\nEricsson 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.\nWhat Will You Do:\nDesign, build and maintain end-to-end ML pipelines for data ingestion, model training, evaluation, deployment and monitoring.\nDeploy ML models as scalable microservices and APIs with high availability and low latency.\nAutomate infrastructure provisioning using Terraform, AWS CloudFormation or similar tools.\nBuild and manage CI/CD pipelines for model deployment, versioning and rollback.\nIntegrate ML models into cloud-native architectures using AWS services such as SageMaker, EKS, Lambda, S3, ECS and CloudWatch.\nWork with DevOps and platform teams to improve consistency, observability and cost efficiency.\nImplement monitoring and alerting for model performance, data drift and system health.\nDevelop reusable microservices, APIs and SDKs for ML model integration.\nMaintain strong standards for code quality, testing and documentation.\nStay updated on developments in MLOps, cloud computing and distributed model serving.\nWhat Will You Bring:\n5-8 years of experience in ML Engineering, Software Engineering or DevOps, with a focus on machine learning systems.\nStrong Python skills and experience with TensorFlow, PyTorch or Scikit-learn.\nStrong experience with AWS services including SageMaker, Lambda, ECS/EKS, CloudWatch, S3 and IAM.\nExperience deploying ML models into production using FastAPI, Flask, gRPC or similar frameworks.\nHands-on experience with Terraform and Infrastructure as Code.\nExperience with Docker and Kubernetes.\nGood understanding of CI/CD pipelines, model registries and artifact management.\nKnowledge of model monitoring, logging and observability tools such as Prometheus, Grafana or Evidently AI.\nStrong software engineering practices including version control, modular design, automated testing and code reviews.\nPreferred Qualifications:\nBachelor’s or Master’s degree 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