{"id":4244364,"url":"https://alion.io/job/zimmer-biomet-mlops-engineer","title":"MLOps Engineer","company":{"id":9247,"name":"Zimmer Biomet","domain":"zimmerbiomet.com","url":"https://alion.io/company/zimmer-biomet","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Phenom","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","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":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Bicep","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Dagster","optional":false},{"name":"Docker","optional":false},{"name":"Feast","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Grafana","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prefect","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-13T12:21:18Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T12:29:14Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient’s mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds. As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talent team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels included, respected, empowered and recognised.What You Can Expect\nJob Summary\nThe MLOps Engineer is responsible for designing, building, and operating scalable, reliable, and secure machine learning platforms and pipelines. This role bridges data science, software engineering, and cloud infrastructure, enabling models to move from experimentation to production with high availability, governance, and performance.The role focuses on ML platform engineering, automation, reliability, and lifecycle management across training, deployment, monitoring, and retraining of machine learning models.\nWork Location: Bangalore\nWork Mode: Hybrid (3 Days in office)\nHow You'll Create Impact\nKey Responsibilities\nML Platform & Pipeline Engineering\nDesign, build, and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring\nProductionize machine learning models developed by Data Scientists\nImplement standardized workflows for feature engineering, model versioning, and model promotion\nDeployment, Monitoring & Reliability\nDeploy models using containerized and cloud-native architectures\nImplement monitoring for model performance, data drift, and system health\nLead root-cause analysis for model or pipeline failures and implement long-term fixes\nAutomation & DevOps for ML\nBuild CI/CD pipelines for ML workflows (training, testing, deployment)\nAutomate infrastructure provisioning and environment management\nEnforce repeatability, reproducibility, and traceability of ML experiments\nGovernance, Security & Compliance\nImplement ML governance controls including lineage, auditability, and access control\nPartner with Security, GRC, and Data Governance teams to ensure compliance\nSupport responsible AI practices and enterprise standards\nCollaboration & Enablement\nPartner closely with Data Scientists, Data Engineers, and Platform Engineers\nProvide guidance and best practices for scalable model development\nContribute to documentation, standards, and internal enablement\nWhat Makes You Stand Out\nTechnologies & Tools\nMachine Learning & MLOps\nPython (primary), with ML libraries (scikit-learn, TensorFlow, PyTorch - support level)\nMLflow, Kubeflow, or similar ML lifecycle tools\nFeature stores (e.g., Feast, cloud-native feature stores)\nModel registries and experiment tracking\nData & Pipeline Engineering\nWorkflow orchestration tools (e.g., Airflow, Dagster, Prefect)\nData processing frameworks (Spark, distributed data processing concepts)\nSQL and data warehousing fundamentals\nCloud & Infrastructure\nCloud platforms: AWS, Azure, or GCP (at least one)\nContainerization: Docker\nOrchestration: Kubernetes\nInfrastructure as Code: Terraform, ARM/Bicep, or CloudFormation\nDevOps & CI/CD\nCI/CD tools (GitHub Actions, GitLab CI, Azure DevOps, Jenkins)\nVersion control: Git\nMonitoring and logging (Prometheus, Grafana, Cloud-native monitoring tools)\nSecurity & Governance\nIdentity and access management (RBAC, secrets management)\nData privacy and model governance concepts\nExposure to regulated or SOX-controlled environments (preferred)\nYour Background\nRequired Qualifications\nEducation\nBachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)\nYears of Experience\n5-8 years of experience in software engineering, data engineering, or platform engineering\n3+ years of hands-on experience in MLOps, ML platform engineering, or ML deployment\nExperience supporting production-grade machine learning systems\nPreferred Qualifications\n7+ years total engineering experience\nExperience supporting real-time or near-real-time ML inference\nExperience with model monitoring, drift detection, and retraining automation\nExperience working in enterprise or regulated environments\nCertifications in cloud platforms or data/ML engineering (preferred)\nCore Competencies\nStrong systems and platform engineering mindset\nAdvanced troubleshooting and problem-solving skills\nAbility to translate research models into reliable production systems\nClear communication across Data Science, Engineering, and IT\nStrong ownership for reliability, scalability, and security\nExperience operating in cloud-based, 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