{"id":1223718,"url":"https://alion.io/job/infosys-mlops-engineer","title":"MLOps Engineer","company":{"id":223,"name":"Infosys","domain":"infosys.com","url":"https://alion.io/company/infosys","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":95,"open_postings":230,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":93,"computed_at":"2026-10-31T05:58:55Z"}},"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":23000,"max_usd":48000,"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 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":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Physical AI","optional":true}],"status":"live","first_seen_at":"2026-08-01T07:02:22Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-11-01T04:39:13Z","board_verified":true,"closed_at":null,"days_open":91,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":90},"description":"We are seeking an experienced MLOps Engineer with strong expertise in deploying, managing, and optimizing machine learning workloads in production environments. This role is primarily focused on MLOps (60%), supported by AWS Cloud (25%) and DevOps (15%) capabilities. The ideal candidate will have hands-on ownership of the end-to-end ML lifecycle, including model training, deployment, monitoring, automation, performance optimization, and retraining. Strong experience with AWS services, CI/CD pipelines, containerization, infrastructure automation, and production-grade ML platforms is essential. This is not a generic DevOps role; candidates must demonstrate proven experience in operationalizing and maintaining ML models at scale in cloud environments.\nResponsibilities\n5+ yrs DevOps/Cloud/MLOps experience; Python for scripting and automation\nStrong with Jenkins, Git, Docker, EKS troubleshooting\nAWS: SageMaker, Lambda, S3, ECS, IAM, RDS, infra creation\nIaC: CloudFormation (CFT) and Terraform\nMLOps: build/operate ML pipelines deploying to SageMaker, Databricks, or Lambda\nTechnical requirements\n1. SageMaker\n2. MLflow\n3. Kubeflow\n4. 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