New opportunity

Senior Systems Engineer - Data DevOps/MLOps

EPAM Systems · Gurgaon, India

About this role

The Senior Systems Engineer - Data DevOps/MLOps will join the EPAM India team to build enterprise-level solutions for global brands. This role focuses on designing, deploying, and managing extensive data and machine learning pipelines to support organizational objectives. Responsibilities include developing CI/CD pipelines for data integration and ML model deployment, setting up cloud-based infrastructure for data processing and model training, and automating data validation, transformation, and workflow orchestration. The engineer will collaborate with data scientists and product teams to integrate ML models into production, enhance model serving and monitoring, and manage data versioning and experiment reproducibility. Additionally, the role involves ensuring infrastructure resilience, implementing security protocols, and troubleshooting pipeline lifecycles. Requirements include a Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field and at least 5 years of experience in Data DevOps or MLOps. Candidates must be proficient in cloud platforms (Azure, AWS, or GCP), IaC tools (Terraform, CloudFormation, or Ansible), containerization (Docker, Kubernetes), and data processing frameworks (Apache Spark, Databricks). Strong programming skills in Python with experience in libraries like Pandas, TensorFlow, and PyTorch are required, alongside familiarity with CI/CD tools (Jenkins, GitLab, GitHub Actions) and MLOps platforms (MLflow, Kubeflow). Monitoring experience with Prometheus and Grafana is essential. Preferred qualifications include knowledge of DataOps tools like Airflow and dbt, data governance platforms like 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/CDCloud PlatformsAzureAWSGCPInfrastructure as CodeTerraformCloudFormationAnsibleDockerKubernetesApache SparkDatabricksPythonPandasTensorFlowPyTorchJenkinsGitLab CI/CDGitHub ActionsGitMLflowKubeflowPrometheusGrafana

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 Automation
The core requirement is to design and manage CI/CD pipelines specifically for ML model deployment and data integration. You must demonstrate the ability to automate the entire lifecycle, from data validation and transformation to model serving, ensuring reproducibility and lineage tracking using tools like MLflow or Kubeflow.
Cloud Infrastructure and IaC Management
You are expected to provision and maintain scalable cloud environments (AWS, Azure, or GCP) using Infrastructure as Code (Terraform, Ansible, or CloudFormation). This priority focuses on building resilient, secure, and automated infrastructure that supports heavy data processing and model training workloads.
Cross-Functional Collaboration and Integration
A critical aspect of this role is acting as the bridge between data scientists and product teams. You must be prepared to facilitate the smooth transition of ML models from experimental environments into production, while simultaneously implementing robust monitoring and alerting systems using Prometheus and Grafana to ensure performance.

Likely challenges to think through

Balancing Model Reproducibility with Scalability
Maintaining strict data versioning and experiment reproducibility while scaling infrastructure for enterprise-level data processing is a significant technical hurdle. Prepare by discussing strategies for managing large-scale data lineage and how you handle the trade-offs between complex tracking overhead and system performance.
Navigating Heterogeneous Tooling Environments
The role requires proficiency across a wide array of frameworks (Spark, Databricks, Kubernetes, Python libraries). The challenge lies in integrating these disparate technologies into a cohesive, secure pipeline. Prepare by highlighting your experience in standardizing workflows across different cloud providers and container orchestration platforms.

Practice your answers

Technical practice

Designing CI/CD for ML Pipelines

What to coverExplain how you would structure a CI/CD pipeline that handles both code changes and model retraining triggers. Discuss how you integrate automated testing for data validation and model performance metrics before deployment.

Infrastructure as Code (IaC) Strategy

What to coverCompare the use of Terraform versus Ansible for provisioning cloud-based data processing infrastructure. Describe a scenario where you would choose one over the other to ensure environment reproducibility.

Container Orchestration with Kubernetes

What to coverHow do you manage resource allocation and scaling for heavy data processing workloads in Kubernetes? Address how you handle persistent storage and stateful data processing requirements within a containerized environment.

ML Model Serving and Monitoring

What to coverDescribe your approach to monitoring production ML models using Prometheus and Grafana. What specific metrics do you track to detect model drift or performance degradation, and how do you trigger automated alerts?

Data Versioning and Lineage

What to coverExplain the importance of data versioning and lineage tracking in MLOps. How do you ensure experiment reproducibility when working with large-scale datasets in environments like Databricks or Spark?

Optimizing Apache Spark Workloads

What to coverDiscuss common performance bottlenecks in Apache Spark jobs and how you troubleshoot them. Mention specific techniques for optimizing memory management and shuffle operations in large-scale data transformations.

Security in Data Pipelines

What to coverWhat security protocols do you implement to ensure data integrity and compliance within cloud-based ML pipelines? Discuss strategies for managing secrets, encryption at rest/transit, and access control.

MLOps Platform Integration

What to coverCompare the roles of MLflow and Kubeflow in an enterprise MLOps ecosystem. How do you decide which platform features to leverage for model registry versus workflow orchestration?

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 regarding model complexity versus production stability?

Troubleshooting Complex Pipeline Failures

What to coverProvide an example of a critical failure in a production data pipeline. Explain your systematic approach to identifying the root cause, communicating the impact, and implementing a long-term fix.

Driving Infrastructure Improvements

What to coverTell us about a time you identified a scalability or resilience issue in an existing infrastructure. What steps did you take to propose and implement the necessary enhancements?

Managing Technical Documentation

What to coverHow do you ensure that complex data and ML workflows are well-documented for team members with varying levels of technical expertise? Give an example of how your documentation helped resolve a recurring issue.

Sharpen your knowledge

Topics to prepare

MLOps Lifecycle and Reproducibility
Focus on the end-to-end operationalization of machine learning models. Review strategies for data versioning, lineage tracking, and ensuring experiment reproducibility. This is critical for the role's requirement to manage the full ML pipeline lifecycle and collaborate with data scientists.
Infrastructure as Code (IaC) and Resilience
Study the principles of declarative infrastructure management using tools like Terraform or Ansible. Focus on designing resilient, scalable cloud environments for data processing and model training, which is a core responsibility for maintaining production-grade ML systems.
CI/CD for Data and ML Pipelines
Examine the differences between traditional software CI/CD and MLOps pipelines, specifically regarding data validation, automated testing of ML models, and deployment strategies. This is essential for the requirement to automate data integration and model deployment.

Skills to revise

Container Orchestration with Kubernetes
Practice deploying and managing containerized ML workloads using Kubernetes. Focus on scaling model serving endpoints and managing resource allocation for training jobs, as this is a primary requirement for the role's infrastructure responsibilities.
Monitoring and Observability with Prometheus/Grafana
Build a dashboard to monitor pipeline health, model drift, and system performance metrics. This aligns with the requirement to enhance model serving and dependability through robust logging and alerting systems.
Data Processing with Apache Spark
Develop a sample data transformation pipeline using PySpark. Focus on efficient data manipulation and integration, which is a key requirement for the role's focus on large-scale data processing frameworks.

Your preparation checklist

Prepare a technical project walkthrough
Select a past project where you designed an end-to-end ML pipeline. Be prepared to explain how you handled model versioning, automated CI/CD deployment, and infrastructure provisioning using IaC tools like Terraform.
Demonstrate MLOps platform expertise
Review your experience with MLflow or Kubeflow. Prepare to discuss how you have used these tools to ensure experiment reproducibility and manage the lifecycle of models from training to production serving.
Review cloud-native monitoring strategies
Prepare examples of how you have implemented Prometheus and Grafana to monitor pipeline performance. Focus on how you set up alerts for data drift or infrastructure failures in a production environment.
Refine your Python and data framework proficiency
Be ready to discuss how you optimize data processing tasks using Apache Spark or Databricks and how you integrate these with Python libraries like Pandas or PyTorch for model training workflows.
Formulate questions for the interviewers
Prepare thoughtful questions regarding the team's current CI/CD maturity, how they handle data governance and security compliance, and the specific cloud architecture challenges they are currently solving for global clients.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…