New opportunity

Senior Systems Engineer - Data DevOps/MLOps

EPAM Systems · Chennai, India

About this role

The Senior Systems Engineer - Data DevOps/MLOps will architect, implement, and oversee large-scale data and machine learning pipelines. This role focuses on bridging the gap between data engineering and production ML model deployment to support enterprise-level solutions. Responsibilities: Build and manage CI/CD pipelines for data integration and ML model rollouts; establish cloud-based infrastructure for data processing and model training; automate data validation, transformation, and workflow orchestration; collaborate with data scientists and product teams for seamless model integration; enhance model serving, monitoring, and reliability; manage data versioning, lineage, and experiment reproducibility; and ensure robust security and regulatory compliance. Requirements: Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field; minimum 5 years of experience in Data DevOps, MLOps, or comparable roles; proficiency in cloud platforms (Azure, AWS, or GCP); expertise in Infrastructure as Code (Terraform, CloudFormation, or Ansible); strong knowledge of containerization (Docker, Kubernetes); experience with data frameworks (Apache Spark, Databricks); and programming skills in Python with ML libraries (Pandas, TensorFlow, PyTorch). Candidates should also be familiar with CI/CD tools (Jenkins, GitLab, GitHub Actions), version control, and MLOps platforms (MLflow, Kubeflow), as well as monitoring tools (Prometheus, Grafana). Preferred qualifications include experience with DataOps principles (Airflow, dbt), data governance (Collibra), Big Data technologies (Hadoop, Hive), and relevant cloud or data engineering certifications.

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

Skills for this role

Data DevOpsMLOpsCI/CDAzureAWSGCPTerraformCloudFormationAnsibleDockerKubernetesApache SparkDatabricksPythonPandasTensorFlowPyTorchJenkinsGitLab CI/CDGitHub ActionsGitMLflowKubeflowPrometheusGrafanaAirflowdbtCollibraHadoopHive

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 MLOps Pipeline Orchestration
The role requires architecting and managing the full lifecycle of ML models, from data integration to production deployment. You must demonstrate the ability to build robust CI/CD pipelines that ensure model reproducibility, lineage tracking, and automated validation.
Cloud Infrastructure and Scalability
You are expected to establish and maintain cloud-based infrastructure (Azure, AWS, or GCP) for data processing and model training. Proficiency in Infrastructure as Code (Terraform, Ansible) is critical to ensure that environments are scalable, resilient, and consistent across development and production.
Cross-Functional Collaboration
Success depends on bridging the gap between data scientists and production engineering teams. You must be able to translate complex ML requirements into reliable production systems while maintaining high standards for model serving, monitoring, and security compliance.

Likely challenges to think through

Managing Model Drift and Pipeline Reliability
Maintaining performance and reliability in production ML environments is difficult due to data drift and infrastructure complexity. Prepare by discussing your experience with monitoring tools like Prometheus and Grafana to proactively diagnose and resolve issues within the pipeline lifecycle.
Balancing Automation with Data Governance
Automating workflows while ensuring strict data lineage and regulatory compliance can create friction. Be prepared to explain how you implement automated guardrails and versioning (using tools like MLflow or Kubeflow) without sacrificing the speed of the deployment pipeline.

Practice your answers

Technical practice

Designing CI/CD for ML Pipelines

What to coverExplain how you would structure a CI/CD pipeline to handle both code changes and model retraining. Discuss the trade-offs between using tools like Jenkins versus cloud-native solutions like GitHub Actions for orchestrating these workflows.

Infrastructure as Code (IaC) Strategy

What to coverDescribe your approach to managing cloud infrastructure for large-scale data processing using Terraform or Ansible. Focus on how you ensure environment consistency and reproducibility across development, staging, and production.

Container Orchestration in MLOps

What to coverHow do you leverage Kubernetes to scale model training and serving? Address how you handle resource allocation, auto-scaling, and persistent storage requirements for data-intensive workloads.

Data Lineage and Versioning

What to coverDiscuss the importance of data lineage and versioning in production ML environments. Explain how you would implement these practices using tools like MLflow or Kubeflow to ensure experiment reproducibility.

Monitoring and Reliability for ML Models

What to coverWhat metrics are critical for monitoring model performance in production beyond standard system health? Explain how you would use Prometheus and Grafana to detect model drift or degradation.

Optimizing Data Processing with Spark

What to coverDescribe a scenario where you optimized a data pipeline using Apache Spark or Databricks. Focus on how you addressed bottlenecks in data transformation or workflow orchestration.

Security and Compliance in Data Pipelines

What to coverHow do you enforce security and regulatory compliance within automated data pipelines? Discuss strategies for managing secrets, data encryption, and access control in a cloud-native environment.

Python for ML Engineering

What to coverExplain how you utilize libraries like Pandas, TensorFlow, or PyTorch in a production context. Focus on writing efficient, maintainable code that integrates well with automated deployment workflows.

Behavioural practice

Cross-functional Collaboration

What to coverDescribe a situation where you had to bridge the gap between data scientists and software engineers to deploy a model. How did you resolve conflicting priorities or technical requirements?

Troubleshooting Complex Pipeline Failures

What to coverProvide an example of a time you diagnosed a critical failure in a data or ML pipeline. What was your systematic approach to identifying the root cause and implementing a long-term fix?

Managing Technical Debt in MLOps

What to coverTell me about a time you identified an opportunity to improve deployment workflows or infrastructure resilience. How did you advocate for these changes and manage the transition without disrupting production?

Adapting to New Technologies

What to coverDescribe a project where you had to quickly learn and implement a new tool or framework, such as a new cloud service or data governance platform. How did you ensure successful adoption and integration?

Sharpen your knowledge

Topics to prepare

MLOps Lifecycle and Reproducibility
Focus on the end-to-end management of machine learning models, specifically data versioning, lineage tracking, and experiment reproducibility. Exercise: Design a conceptual architecture for a model registry that ensures every production model can be traced back to its specific training dataset and code version using tools like MLflow.
CI/CD for Data and ML Pipelines
Understand the integration of automated testing and deployment for data pipelines and ML models. Exercise: Map out a CI/CD workflow that includes automated data validation steps before model retraining, ensuring that drift detection triggers a new deployment cycle.
Infrastructure as Code (IaC) for Data Platforms
Master the principles of provisioning cloud infrastructure programmatically. Exercise: Create a Terraform configuration template that deploys a scalable Kubernetes cluster and a managed Spark environment, emphasizing modularity and security compliance.

Skills to revise

Container Orchestration with Kubernetes
Gain proficiency in managing containerized ML workloads. Exercise: Write a Kubernetes manifest to deploy a Python-based model serving container, including resource limits and horizontal pod autoscaling configurations.
Data Processing with Apache Spark
Develop expertise in large-scale data transformation. Exercise: Write a PySpark script that performs data cleaning and feature engineering on a large dataset, ensuring the code is optimized for execution on a Databricks or similar cluster environment.
Monitoring and Observability
Implement robust monitoring for production systems. Exercise: Configure a Prometheus exporter to track custom metrics from an ML model serving endpoint and create a Grafana dashboard to visualize latency and prediction accuracy trends.

Your preparation checklist

Prepare a technical walkthrough of an end-to-end MLOps pipeline
Develop a concise presentation or document detailing a project where you automated the deployment of an ML model. Highlight your use of CI/CD tools (e.g., Jenkins or GitHub Actions), containerization (Docker/Kubernetes), and MLOps platforms like MLflow or Kubeflow to ensure reproducibility and lineage.
Demonstrate Infrastructure as Code (IaC) proficiency
Prepare to discuss specific scenarios where you used Terraform, Ansible, or CloudFormation to provision cloud infrastructure on AWS, Azure, or GCP. Focus on how you maintained scalability and security compliance within your infrastructure configurations.
Review data engineering and processing frameworks
Be ready to explain your experience with Apache Spark or Databricks for large-scale data processing. Prepare examples of how you integrated these frameworks into production pipelines to handle data transformation and validation tasks.
Formulate questions regarding EPAM's MLOps maturity
Prepare thoughtful questions for the interviewers about their current stack, such as how they handle model monitoring with Prometheus/Grafana, their approach to data governance, or how they currently bridge the collaboration gap between data scientists and production engineering teams.
Map your experience to DataOps and governance principles
Review your experience with DataOps tools like Airflow or dbt and data governance concepts. Even if you have limited exposure, prepare to discuss how you would implement these to improve data quality and lineage in an enterprise environment.

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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