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Senior Systems Engineer - Data DevOps/MLOps

EPAM Systems · Hyderabad, India

About this role

EPAM Systems is seeking a Senior Systems Engineer specializing in Data DevOps and MLOps to join their team. This role focuses on building enterprise-level solutions, specifically in data engineering, pipeline automation, and the deployment of machine learning models into production environments. Responsibilities: Build and maintain CI/CD pipelines for data integration and ML model deployment. Manage cloud-based infrastructure for data processing and model training. Automate data validation, transformation, and workflow orchestration. Collaborate with data scientists and product teams to productionize ML models. Optimize model serving, monitoring, and performance. Ensure data versioning, lineage tracking, and reproducibility. Enhance infrastructure scalability, resilience, and security compliance. Troubleshoot issues across the data and ML lifecycle. Requirements: Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field. At least 5 years of experience in Data DevOps, MLOps, or equivalent roles. Proficiency in cloud platforms (Azure, AWS, or GCP) and Infrastructure as Code (Terraform, CloudFormation, or Ansible). Strong skills in containerization (Docker, Kubernetes) and data processing frameworks (Apache Spark, Databricks). Solid Python programming skills and familiarity with libraries like Pandas, TensorFlow, or PyTorch. Experience with CI/CD tools (Jenkins, GitLab CI/CD, GitHub Actions), Git, and MLOps platforms (MLflow, Kubeflow). Experience with monitoring tools like Prometheus or Grafana. Strong analytical, problem-solving, and communication skills. Nice to have: Exposure to DataOps tools (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 DevOpsMLOpsMLflowKubeflowCI/CDInfrastructure as CodeDockerKubernetesApache SparkDatabricksPythonPandasTensorFlowPyTorchJenkinsGitGitHub ActionsTerraformAnsiblePrometheusGrafanaCloud ComputingAzureAWSGCP

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 build and maintain CI/CD pipelines that bridge the gap between data engineering and production ML model deployment. You must demonstrate the ability to automate the entire lifecycle, including data validation, transformation, and workflow orchestration to ensure seamless model delivery.
Cloud Infrastructure and Scalability
You are expected to manage cloud-based infrastructure (AWS, Azure, or GCP) using Infrastructure as Code (IaC) tools like Terraform or Ansible. The priority is to design resilient, scalable environments that support high-performance data processing and model training while maintaining security compliance.
Cross-Functional Collaboration and Reproducibility
Success in this role depends on partnering with data scientists and product teams to productionize models. You must implement robust data versioning, lineage tracking, and reproducibility standards to ensure that ML experiments can be reliably transitioned into live operational systems.

Likely challenges to think through

Balancing Model Performance with Operational Stability
A common challenge in MLOps is optimizing model serving for low latency while ensuring the underlying infrastructure remains stable and cost-effective. Prepare by discussing your experience with monitoring tools like Prometheus or Grafana to detect performance bottlenecks and your approach to troubleshooting complex pipeline failures.
Managing Technical Debt in Rapid ML Cycles
The need for rapid deployment often conflicts with the requirement for rigorous data governance and security compliance. Be prepared to explain how you maintain documentation and lineage tracking without slowing down the development velocity of data science teams.

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 data integration and ML model deployment. Focus on how you ensure reproducibility, automated testing for data quality, and the transition from model training to production serving.

Infrastructure as Code (IaC) Strategy

What to coverCompare Terraform, CloudFormation, and Ansible in the context of managing cloud-based infrastructure for data processing. Discuss how you maintain state and ensure security compliance across multi-environment deployments.

Containerization and Orchestration in MLOps

What to coverDescribe your approach to containerizing ML models using Docker and orchestrating them with Kubernetes. Address how you handle resource allocation, scaling, and dependency management for complex ML workloads.

Data Versioning and Lineage

What to coverHow do you implement data versioning and lineage tracking to ensure reproducibility in ML experiments? Discuss the tools or methodologies you use to track the relationship between raw data, processed features, and specific model versions.

Optimizing Model Serving and Monitoring

What to coverDetail your strategy for monitoring ML models in production using tools like Prometheus or Grafana. Explain how you detect model drift and performance degradation, and what automated actions you might trigger in response.

Workflow Orchestration with Apache Spark/Databricks

What to coverDiscuss the challenges of automating data transformation and workflow orchestration within a Spark or Databricks environment. How do you ensure these workflows are resilient and scalable?

Troubleshooting the ML Lifecycle

What to coverDescribe a scenario where an ML pipeline failed in production. Explain your systematic approach to identifying whether the issue originated from data quality, infrastructure configuration, or the model code itself.

Security and Compliance in Data Pipelines

What to coverHow do you integrate security protocols into your data and ML pipelines? Address how you handle sensitive data, access control, and compliance requirements while maintaining high-performance automation.

Behavioural practice

Cross-functional Collaboration

What to coverDescribe a time you worked closely with data scientists to productionize a model. How did you bridge the gap between their experimental code and the requirements for a stable, scalable production environment?

Handling Technical Debt in Infrastructure

What to coverProvide an example of when you identified a bottleneck or scalability issue in an existing data pipeline. Explain the steps you took to improve the system's resilience while minimizing disruption to ongoing operations.

Communication of Technical Complexity

What to coverGive an example of how you explained a complex technical trade-off (e.g., choosing a specific cloud architecture or tool) to non-technical stakeholders or product teams to gain their alignment.

Managing Ambiguity in Projects

What to coverDescribe a situation where you had to build a solution for a new data or ML requirement with limited initial documentation. How did you approach the problem, gather necessary information, and deliver a reliable result?

Sharpen your knowledge

Topics to prepare

MLOps Lifecycle and Reproducibility
Focus on the end-to-end ML lifecycle, specifically data versioning, lineage tracking, and ensuring model reproducibility. Exercise: Design a conceptual architecture for a model training pipeline that incorporates automated data versioning and model registry integration using tools like MLflow to ensure experiment tracking.
Infrastructure as Code (IaC) for Data Platforms
Understand how to manage cloud-based infrastructure for data processing and ML training using declarative code. Exercise: Draft a Terraform or CloudFormation template that provisions a scalable cluster (e.g., Spark or Kubernetes) and define the security groups required for production compliance.
CI/CD for Data and ML Pipelines
Master the integration of automated testing and deployment for data pipelines and ML models. Exercise: Create a CI/CD workflow diagram that triggers automated data validation, model retraining, and deployment to a staging environment upon a Git push event.

Skills to revise

Container Orchestration with Kubernetes
Develop proficiency in deploying and managing containerized ML workloads. Exercise: Write a Kubernetes deployment manifest for a model serving container, including resource limits, liveness probes, and horizontal pod autoscaling configurations.
Python for Data Engineering
Utilize Python libraries for data transformation and ML model integration. Exercise: Build a script using Pandas to perform automated data quality checks (e.g., null value detection, schema validation) as a pre-processing step in a pipeline.
Monitoring and Observability
Implement monitoring for production ML systems using Prometheus and Grafana. Exercise: Define custom metrics for a model serving endpoint (e.g., latency, prediction drift) and configure a sample Grafana dashboard to visualize these performance indicators.

Your preparation checklist

Prepare a technical case study on MLOps lifecycle management
Create a brief presentation or document detailing a past 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 how you ensured model reproducibility and lineage tracking.
Demonstrate Infrastructure as Code (IaC) proficiency
Prepare a code repository or walkthrough showing how you have used Terraform or Ansible to provision and manage cloud infrastructure (AWS, Azure, or GCP) for data processing or model training environments, emphasizing scalability and security compliance.
Develop a monitoring and troubleshooting strategy
Be ready to explain how you have implemented observability in production ML systems using tools like Prometheus or Grafana. Prepare a specific example of a time you identified and resolved a performance bottleneck or data drift issue in a live pipeline.
Formulate questions regarding EPAM's MLOps maturity
Prepare thoughtful questions for the interviewers about their current stack, such as: 'How does the team currently balance data governance with the need for rapid model experimentation?' or 'What are the primary challenges the team faces when scaling model serving in your current cloud 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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