New opportunity

MLOps Engineer

ZB Global Services India Pvt Ltd · Bangalore, India

About this role

The MLOps Engineer is responsible for designing, building, and operating scalable, reliable, and secure machine learning platforms and pipelines at Zimmer Biomet. This role bridges data science, software engineering, and cloud infrastructure to enable the transition of models from experimentation to production with high availability, governance, and performance. Key Responsibilities: ML Platform & Pipeline Engineering: Design and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring; productionize models; implement workflows for feature engineering and model versioning. Deployment, Monitoring & Reliability: Deploy models using containerized architectures; implement monitoring for performance, data drift, and system health; lead root-cause analysis for failures. Automation & DevOps for ML: Build CI/CD pipelines for ML workflows; automate infrastructure provisioning; enforce reproducibility and traceability. Governance, Security & Compliance: Implement ML governance controls including lineage and access control; partner with Security and GRC teams; support responsible AI practices. Collaboration & Enablement: Partner with Data Scientists and Data Engineers; provide guidance on scalable model development. Required Qualifications: Bachelor’s degree in Computer Science, Engineering, Data Science, or related field. 5–8 years of experience in software engineering, data engineering, or platform engineering, including 3+ years of hands-on experience in MLOps, ML platform engineering, or ML deployment. Experience supporting production-grade machine learning systems. Preferred Qualifications: 7+ years total engineering experience; experience with real-time ML inference, model monitoring, and drift detection; experience in regulated or SOX-controlled environments; relevant cloud or ML engineering certifications. Work Mode: Hybrid (3 days in office).

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Pythonscikit-learnTensorFlowPyTorchMLflowKubeflowFeastAirflowDagsterPrefectSparkSQLAWSAzureGCPDockerKubernetesTerraformARMBicepCloudFormationGitHub ActionsGitLab CIAzure DevOpsJenkinsGitPrometheusGrafana

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End ML Pipeline Engineering
The core requirement is to design and maintain robust pipelines that handle the full lifecycle of machine learning models, including training, validation, deployment, and monitoring. You must demonstrate the ability to transition models from research-oriented experimentation into high-availability production environments.
ML Governance and Compliance
Given the focus on regulated or SOX-controlled environments, you must implement strict governance controls. This includes ensuring model lineage, auditability, and secure access management while partnering with GRC and Security teams to meet enterprise compliance standards.
Automation and Infrastructure Scalability
You are expected to automate infrastructure provisioning and CI/CD workflows for ML. This requires proficiency in Infrastructure as Code (Terraform, CloudFormation) and container orchestration (Kubernetes) to ensure reproducibility and scalability of ML systems.
Cross-Functional Collaboration
The role acts as a bridge between Data Scientists and IT/Engineering. You must provide technical guidance on scalable model development and contribute to documentation and standards that enable the broader organization to adopt best practices.

Likely challenges to think through

Balancing Innovation with Strict Governance
Operating in a regulated or SOX-controlled environment often introduces friction between rapid model iteration and compliance requirements. Prepare by discussing how you have implemented automated guardrails that satisfy security audits without stifling the agility of data science teams.
Managing Model Drift in Production
The role requires implementing monitoring for performance and data drift. The challenge lies in defining actionable thresholds for retraining. Prepare by explaining your approach to setting up automated alerts and feedback loops that distinguish between transient noise and actual model degradation.
Standardizing Heterogeneous ML Workflows
Data scientists often use diverse tools and frameworks. Creating a unified platform that supports these variations while maintaining reproducibility is difficult. Prepare by discussing your experience in building standardized, modular pipelines that accommodate different libraries while enforcing consistent versioning and lineage.

Practice your answers

Technical practice

Designing End-to-End ML Pipelines

What to coverExplain your approach to building a scalable pipeline that handles training, validation, and deployment. Discuss how you ensure reproducibility and traceability across these stages using tools like MLflow or Kubeflow.

Model Monitoring and Drift Detection

What to coverDescribe how you implement monitoring for production models to detect data drift or performance degradation. Suggestion: Focus on how you integrate Prometheus and Grafana to alert on system health and model accuracy metrics.

CI/CD for Machine Learning

What to coverHow do you structure CI/CD pipelines to handle both code changes and model retraining triggers? Discuss the trade-offs between automated retraining and human-in-the-loop validation.

Containerization and Orchestration

What to coverExplain the role of Docker and Kubernetes in your MLOps architecture. How do you manage resource allocation and scaling for containerized ML inference services?

Infrastructure as Code (IaC) in MLOps

What to coverDescribe your experience using Terraform or similar tools to automate infrastructure provisioning. Why is IaC critical for maintaining consistent environments across development, staging, and production?

Feature Engineering and Storage

What to coverWhat is the purpose of a feature store like Feast in an MLOps ecosystem? Explain how it helps maintain consistency between training and inference data.

Governance and Security in ML

What to coverHow do you implement lineage and access control for ML models in a regulated environment? Discuss how you ensure compliance with data privacy standards while maintaining model performance.

Troubleshooting Pipeline Failures

What to coverWalk through your process for conducting root-cause analysis when a production ML pipeline fails. Suggestion: Emphasize your methodology for isolating issues between data quality, infrastructure, and model code.

Behavioural practice

Bridging Data Science and Engineering

What to coverDescribe a situation where you had to help a Data Scientist productionize a model that was not initially scalable. How did you communicate the necessary changes while maintaining a collaborative relationship?

Managing Technical Debt in ML Systems

What to coverGive an example of a time you identified a bottleneck or technical debt in an existing ML platform. What actions did you take to resolve it, and what was the impact on system reliability?

Handling Conflicting Priorities

What to coverTell me about a time you had to balance the need for rapid model deployment with the requirement for strict governance and security controls. How did you reach a compromise?

Ownership of Production Reliability

What to coverDescribe a high-stakes incident where a production ML system failed. What was your specific role in the resolution, and what long-term measures did you implement to prevent recurrence?

Sharpen your knowledge

Topics to prepare

ML Model Governance and Compliance
The role requires implementing lineage, auditability, and access control in regulated environments. Exercise: Design a documentation strategy for a model's lifecycle that tracks data provenance, training parameters, and deployment approvals to satisfy SOX-style audit requirements.
ML Pipeline Reliability and Monitoring
Focus on productionizing models with drift detection and system health monitoring. Exercise: Define a root-cause analysis framework for a scenario where a deployed model's prediction accuracy degrades due to feature data drift, identifying specific metrics to monitor in Prometheus or Grafana.

Skills to revise

Infrastructure as Code (IaC)
The role requires automating infrastructure provisioning. Exercise: Write a Terraform or CloudFormation script to deploy a scalable, containerized environment on your preferred cloud provider that includes a private network, a Kubernetes cluster, and managed storage.
CI/CD for Machine Learning
Building automated workflows for training, testing, and deployment is a core responsibility. Exercise: Create a GitHub Actions or GitLab CI pipeline that triggers a model retraining job upon a code commit, runs automated unit tests on the training script, and pushes the resulting model artifact to a registry.
Container Orchestration with Kubernetes
The role involves deploying models using containerized architectures. Exercise: Create a Kubernetes deployment manifest for a Python-based ML inference service, including horizontal pod autoscaling and liveness/readiness probes to ensure high availability.

Your preparation checklist

Prepare a production-grade ML pipeline walkthrough
Develop a concise case study demonstrating an end-to-end ML pipeline you have built. Focus on how you handled model versioning, automated testing, and deployment using containerization (Docker/Kubernetes) and CI/CD tools like GitHub Actions or Jenkins.
Document experience with model monitoring and drift detection
Prepare specific examples of how you implemented monitoring for production models. Be ready to discuss the tools used (e.g., Prometheus, Grafana) and how you identified and addressed data drift or performance degradation in real-time inference scenarios.
Review infrastructure-as-code and cloud governance
Be prepared to explain your experience with Terraform or similar tools to automate infrastructure provisioning. Highlight your approach to security, including RBAC, secrets management, and maintaining compliance in regulated environments.
Formulate questions regarding ML governance and collaboration
Prepare thoughtful questions for the interviewers about how the team currently manages model lineage and auditability, and how they bridge the gap between data science experimentation and production-grade engineering.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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