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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 with deep expertise in Data DevOps and MLOps to build enterprise-level solutions. The role focuses on architecting, implementing, and overseeing large-scale data and machine learning pipelines to support production environments. Responsibilities: Build and oversee CI/CD pipelines for data integration and ML model rollout; 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 ML model integration; improve 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); experience with Infrastructure as Code (Terraform, CloudFormation, or Ansible); strong knowledge of containerization (Docker, Kubernetes); practical experience with data frameworks (Apache Spark, Databricks); programming skills in Python with familiarity in libraries like Pandas, TensorFlow, and PyTorch; and experience with CI/CD tools (Jenkins, GitLab, GitHub Actions) and MLOps platforms (MLflow, Kubeflow). Nice to have: 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 DevOpsMLOpsMLflowKubeflowCI/CDInfrastructure as CodeDockerKubernetesApache SparkDatabricksPythonPandasTensorFlowPyTorchJenkinsGitGitHub ActionsTerraformAnsiblePrometheusGrafanaAirflowdbtCloud Computing

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 core of this role is architecting and maintaining 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 model rollout, ensuring that experiments are reproducible and lineage is tracked.
Cloud-Native Infrastructure Management
You are expected to establish and maintain scalable cloud infrastructure (AWS, Azure, or GCP) using Infrastructure as Code (Terraform, Ansible). This requires deep proficiency in containerization (Docker, Kubernetes) to ensure that data processing and model training environments are resilient, secure, and performant.
Cross-Functional Collaboration and Integration
Success depends on partnering with data scientists and product teams to integrate ML models into production environments. You must be able to translate complex technical requirements into reliable, scalable systems while ensuring model serving and monitoring meet performance standards.

Likely challenges to think through

Balancing Model Reproducibility with Rapid Deployment
A common challenge in MLOps is maintaining strict data versioning and experiment reproducibility while supporting fast-paced CI/CD cycles. Prepare to discuss how you manage the trade-off between rigorous governance and the need for agility in production environments.
Managing Complexity in Heterogeneous Data Environments
With requirements spanning Spark, Databricks, and potentially Big Data technologies like Hadoop/Hive, you will likely face challenges in standardizing workflows across different data frameworks. Be ready to explain how you maintain consistency in monitoring and logging across diverse, large-scale data pipelines.

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 deployment and model retraining. Discuss how you manage the transition from development to production while ensuring model reproducibility and data lineage.

Infrastructure as Code (IaC) for Data Platforms

What to coverDescribe your approach to managing cloud infrastructure for data processing using tools like Terraform or Ansible. Focus on how you ensure environment consistency and scalability when deploying large-scale data frameworks like Apache Spark or Databricks.

Containerization and Orchestration in MLOps

What to coverDiscuss the trade-offs between using Docker and Kubernetes for model serving versus model training. Explain how you handle resource allocation and scaling for compute-intensive ML workloads in a production environment.

Data Validation and Workflow Orchestration

What to coverHow do you automate data validation and transformation steps within a production pipeline? Explain how you would use tools like Airflow to manage complex dependencies and ensure data quality before model training begins.

Model Monitoring and Reliability

What to coverDetail your strategy for monitoring ML models in production using tools like Prometheus and Grafana. Explain how you detect and alert on model drift, performance degradation, or infrastructure failures.

Data Versioning and Lineage

What to coverExplain the importance of data versioning and lineage in the context of experiment reproducibility. Describe how you would implement these practices using MLOps platforms like MLflow or Kubeflow.

Security and Compliance in Data Pipelines

What to coverWhat measures do you take to ensure data integrity and regulatory compliance within automated pipelines? Discuss how you handle sensitive data and access control when integrating ML models into enterprise production environments.

Python for Data Engineering

What to coverDescribe a scenario where you used Python libraries like Pandas or TensorFlow to optimize a data processing or model training task. Focus on efficiency, memory management, and integration with larger data frameworks.

Behavioural practice

Cross-functional Collaboration

What to coverDescribe a situation where you had to collaborate with data scientists to resolve a production integration issue. Explain how you communicated technical constraints and arrived at a solution that met both performance and business requirements.

Problem Solving in Complex Pipelines

What to coverProvide an example of a time you had to diagnose a critical failure across a multi-stage data or ML pipeline. Detail your troubleshooting process, the steps you took to isolate the root cause, and how you prevented recurrence.

Adapting to New Technologies

What to coverDiscuss a time you had to quickly learn and implement a new tool or framework to improve an existing MLOps workflow. Explain how you evaluated the tool's suitability and managed the transition for your team.

Balancing Speed and Reliability

What to coverDescribe a project where you had to balance the need for rapid model deployment with the requirement for robust infrastructure and security. How did you prioritize tasks and ensure the final solution was scalable and resilient?

Sharpen your knowledge

Topics to prepare

MLOps Lifecycle and Reproducibility
Focus on the conceptual framework of model lineage, versioning, and experiment tracking. Exercise: Design a strategy for tracking model artifacts and data versions using MLflow to ensure full experiment reproducibility in a production environment.
Data Pipeline Orchestration and Governance
Understand the principles of DataOps, including workflow orchestration and data lineage. Exercise: Map out a robust CI/CD pipeline for data integration that incorporates automated validation steps and ensures compliance with data governance standards.

Skills to revise

Infrastructure as Code (IaC)
Practice provisioning cloud infrastructure using Terraform or Ansible. Exercise: Create a modular Terraform script to deploy a scalable Kubernetes cluster on a cloud provider (AWS, Azure, or GCP) configured for ML model training workloads.
Containerization and Orchestration
Develop proficiency in Docker and Kubernetes for deploying ML services. Exercise: Containerize a Python-based ML model using Docker and define a Kubernetes deployment manifest that includes resource limits and auto-scaling policies for high availability.
CI/CD for ML Pipelines
Implement automated testing and deployment workflows. Exercise: Configure a GitHub Actions or Jenkins pipeline that triggers automated unit tests for data transformation code and performs a canary deployment of a model to a staging environment.
Data Processing with Spark and Databricks
Gain hands-on experience with large-scale data frameworks. Exercise: Write a PySpark job to perform complex data transformations on a large dataset and optimize the execution plan for performance within a Databricks environment.

Your preparation checklist

Architect an end-to-end MLOps pipeline walkthrough
Prepare a technical case study demonstrating your experience in building a CI/CD pipeline for ML models. Focus on how you integrated tools like MLflow or Kubeflow with cloud infrastructure to ensure model reproducibility, versioning, and automated deployment.
Demonstrate Infrastructure as Code (IaC) proficiency
Create a repository or documentation snippet showing how you use Terraform or Ansible to provision scalable cloud environments for data processing. Explain how you manage state and ensure security compliance within these automated configurations.
Prepare a containerization and orchestration strategy
Be ready to discuss your approach to containerizing ML workloads using Docker and managing them via Kubernetes. Highlight your experience in optimizing resource allocation for data-intensive tasks using Apache Spark or Databricks.
Develop a monitoring and reliability framework
Outline your methodology for monitoring production ML models. Explain how you utilize tools like Prometheus and Grafana to track model drift, performance metrics, and system health to ensure high availability and reliability.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding the team's current data governance practices, how they handle data lineage, and the specific challenges they face when integrating new ML models into their existing production environments.

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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