{"id":3677552,"url":"https://alion.io/job/samsung-electronics-mlops-engineer-2","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":"A","score":85,"open_postings":140,"ghost_share":0.007,"stale_share":0.536,"repost_share":0.057,"time_to_fill_p50_days":36,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":null,"experience_years_min":4,"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":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-11-03T11:35:00Z","employer_posted_date":null,"last_verified_at":"2026-11-03T11:35:00Z","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 our PDx Lead Scoring Model. The role involves automating ML deployment pipelines, implementing governance and monitoring practices, and ensuring reliable production operations.\n\nResponsibilities: \nBuild and maintain CI/CD pipelines for Machine Learning models.\nDevelop and automate ML training and inference pipelines.\nManage model deployment, versioning, registration, and lifecycle tracking.\nImplement model monitoring and observability using AWS services.\nEnsure secure ML deployments through access controls and governance.\nTroubleshoot production issues and ensure platform reliability.\nCollaborate with Data Scientists and Engineering teams to deploy ML models into production.\n\nRequirements: \n4+ years of experience in MLOps, DevOps, or ML Engineering.\nProven hands-on experience deploying and supporting AI/ML models in production.\nStrong understanding of production-grade ML deployment practices.\nAbility to troubleshoot issues and manage end-to-end deployment workflows.\nExperience working with Data Science and Engineering teams in an enterprise environment.\nPreference: Candidates with strong hands-on experience in AWS SageMaker, production MLOps, and ML CI/CD automation will be preferred.\n\nMust-Have Technical Skills: \nAWS SageMaker and AWS ML Ecosystem\nMLOps / Production ML Model Deployment\nCI/CD Pipelines for ML Models\nAutomated ML Training and Inference Pipelines\nPython and Git\nDocker and Containerization\nModel Registry, Versioning, and Experiment Tracking\nAWS CloudWatch and Monitoring\nSecurity, Access Management, and Model Governance\nSkills\nAWS, CI / CD, DevOps, Docker, MLOps, Python, 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