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 adaptable 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 ExpectJob SummaryThe 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.Work Location: BangaloreWork Mode: Hybrid (3 Days in office)How You'll Create ImpactKey ResponsibilitiesML Platform & Pipeline EngineeringDesign, build, and maintain end-to-end ML pipelines for training, validation, deployment, and monitoringProductionize machine learning models developed by Data ScientistsImplement standardized workflows for feature engineering, model versioning, and model promotionDeployment,



Monitoring & ReliabilityDeploy models using containerized and cloud-native architecturesImplement monitoring for model performance, data drift, and system healthLead root-cause analysis for model or pipeline failures and implement long-term fixesAutomation & DevOps for MLBuild CI/CD pipelines for ML workflows (training, testing, deployment)Automate infrastructure provisioning and environment managementEnforce repeatability, reproducibility, and traceability of ML experimentsGovernance, Security & ComplianceImplement ML governance controls including lineage, auditability, and access controlPartner with Security, GRC, and Data Governance teams to ensure complianceSupport responsible AI practices and enterprise standardsCollaboration & EnablementPartner closely with Data Scientists, Data Engineers, and Platform EngineersProvide guidance and best practices for scalable model developmentContribute to documentation, standards, and internal enablementWhat Makes You Stand OutTechnologies & ToolsMachine Learning & MLOpsPython (primary), with ML libraries (scikit-learn, TensorFlow, PyTorch – support level)MLflow, Kubeflow, or similar ML lifecycle toolsFeature stores (e.G., Feast, cloud-native feature stores)Model registries and experiment trackingData & Pipeline EngineeringWorkflow orchestration tools (e.G., Airflow, Dagster, Prefect)Data processing frameworks (Spark,



distributed data processing concepts)SQL and data warehousing fundamentalsCloud & InfrastructureCloud platforms: AWS, Azure, or GCP (at least one)Containerization: DockerOrchestration: KubernetesInfrastructure as Code: Terraform, ARM/Bicep, or CloudFormationDevOps & CI/CDCI/CD tools (GitHub Actions, GitLab CI, Azure DevOps, Jenkins)Version control: GitMonitoring and logging (Prometheus, Grafana, Cloud-native monitoring tools)Security & GovernanceIdentity and access management (RBAC, secrets management)Data privacy and model governance conceptsExposure to regulated or SOX-controlled environments (preferred)Your BackgroundRequired QualificationsEducationBachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)Years of Experience5–8 years of experience in software engineering, data engineering, or platform engineering3+ years of hands-on experience in MLOps, ML platform engineering, or ML deploymentExperience supporting production-grade machine learning systemsPreferred Qualifications7+ years total engineering experienceExperience supporting real-time or near-real-time ML inferenceExperience with model monitoring, drift detection, and retraining automationExperience working in enterprise or regulated environmentsCertifications in cloud platforms or data/ML engineering (preferred)Core CompetenciesStrong systems and platform engineering mindsetAdvanced troubleshooting and problem-solving skillsAbility to translate research models into reliable production systemsClear communication across Data Science, Engineering, and ITStrong ownership for reliability, scalability, and securityExperience operating in cloud-based, distributed environmentsPhysical RequirementsTravel ExpectationsEOE/M/F/Vet/Disability

📌 Cloud Engineer – Aws & Ai/Ml Infrastructure (Bengaluru)
🏢 Aptiv
📍 Bengaluru