{"id":3575517,"url":"https://alion.io/job/samsung-electronics-mlops-engineer","title":"MLOps Engineer","company":{"id":166,"name":"Samsung Electronics","domain":"samsung.com","url":"https://alion.io/company/samsung","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":124,"ghost_share":0.008,"stale_share":0.613,"repost_share":0.024,"time_to_fill_p50_days":35,"computed_at":"2026-11-03T05:54:40Z"}},"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":24000,"max_usd":50000,"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 SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-10-31T20:36:00Z","employer_posted_date":null,"last_verified_at":"2026-10-31T20:36:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"We are looking for an experienced MLOps Engineer to build and maintain a secure, scalable deployment platform for the PDx Model. The role involves automating ML release pipelines, implementing governance and monitoring, and providing production support for the PDx Lead Scoring Model.\n\nResponsibilities: \nBuild and maintain secure ML deployment platforms using AWS SageMaker and related AWS services.\nDevelop automated CI/CD pipelines for ML model training, testing, and deployment.\nAutomate build and release processes for training and inference pipelines.\nImplement model registry, versioning, experiment tracking, and lifecycle management.\nUse Docker for containerization and support reliable ML deployments.\nMonitor production systems using AWS CloudWatch and observability tools.\nImplement security controls, access management, governance, and model lineage.\nTroubleshoot production issues and ensure reliable operation of deployed ML models.\nCollaborate with Data Scientists and engineering teams to transition ML models into production.\nMaintain Python codebases and Git repositories while improving deployment automation.\n\nRequirements: \n4-7 years of engineering experience with hands-on production AI/ML deployments.\nStrong practical experience with AWS SageMaker and ML deployment pipelines.\nProven experience in CI/CD automation and production ML operations.\nStrong troubleshooting, ownership, and problem-solving skills.\nExperience working in enterprise cloud environments.\nExcellent communication and stakeholder collaboration skills.\nImmediate joiners preferred.\n\nKey Skills: \nAWS SageMaker and AWS ML Ecosystem\nMLOps and Machine Learning Deployment\nCI/CD Pipeline Automation\nPython and Git\nDocker and Containerization\nML Training and Inference Pipelines\nModel Registry, Versioning and Experiment Tracking\nAWS CloudWatch and Observability\nSecurity, Access Control and Model Lineage\nProduction Support and Troubleshooting\nSkills\nCI / CD, CloudWatch, Docker, Git, MLOps, Python, 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