The Senior Systems Engineer - Data DevOps/MLOps will join EPAM to build enterprise-level solutions for global brands, focusing on data engineering, pipeline automation, and the deployment of machine learning models into production environments. Responsibilities include building CI/CD pipelines for data integration and ML model deployment, managing cloud-based infrastructure for data processing and training, and automating data validation and workflow orchestration. The role requires partnering with data scientists and software engineers to optimize model serving, maintain data versioning and lineage, and ensure infrastructure resilience and security. Requirements include a Bachelor's or Master's degree in Computer Science or a related field and at least 5 years of experience in Data DevOps or MLOps. Candidates must have hands-on experience with cloud platforms (Azure, AWS, or GCP), Infrastructure as Code (Terraform, CloudFormation, or Ansible), containerization (Docker, Kubernetes), and data processing frameworks (Apache Spark or Databricks). Proficiency in Python and familiarity with ML libraries (Pandas, TensorFlow, PyTorch) and MLOps platforms (MLflow, Kubeflow) are essential. Preferred qualifications include exposure to DataOps tools like Airflow or dbt, knowledge of data governance frameworks like Collibra, and familiarity with Big Data technologies such as Hadoop or Hive. The position is offered as a hybrid role across multiple Indian locations.
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Skills for this role
Data DevOpsMLOpsMLflowKubeflowCI/CDInfrastructure as CodeDockerKubernetesApache SparkDatabricksPythonPandasTensorFlowPyTorchJenkinsGitGitHub ActionsTerraformAnsiblePrometheusGrafanaAirflowdbtHadoopHive
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
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 data validation, transformation, and model serving while ensuring reproducibility and lineage tracking.
Cloud Infrastructure and Orchestration
You are expected to manage scalable cloud-based infrastructure (AWS, Azure, or GCP) using Infrastructure as Code (Terraform, Ansible) and container orchestration (Kubernetes, Docker). The priority is to ensure infrastructure resilience, security, and performance for data processing and model training workloads.
Cross-Functional Collaboration
Success in this role depends on partnering with data scientists and software engineers to optimize model serving and troubleshoot pipeline issues. You must be able to translate complex technical requirements into reliable production systems while maintaining clear documentation.
Likely challenges to think through
Balancing Model Reproducibility with Scalability
Maintaining data versioning and lineage in high-velocity production environments is inherently difficult. Prepare by discussing strategies for managing state in distributed systems and how you balance the need for strict experiment tracking with the performance requirements of large-scale data processing frameworks like Spark or Databricks.
Integrating Diverse Tooling Ecosystems
The role requires familiarity with a wide array of tools (MLflow, Kubeflow, Airflow, dbt). The challenge lies in selecting and integrating the right tools to create a cohesive workflow. Prepare by articulating how you evaluate and implement MLOps platforms to ensure they meet security and compliance standards without creating operational silos.
03
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 triggers. Discuss the challenges of versioning data, model artifacts, and environment configurations to ensure reproducibility.
Infrastructure as Code (IaC) for Data Platforms
What to coverCompare Terraform and Ansible in the context of provisioning cloud-based data infrastructure. Describe how you ensure consistency and security across environments when deploying clusters like Databricks or EMR.
Containerization and Orchestration in MLOps
What to coverDiscuss the trade-offs between using Docker and Kubernetes for model serving versus model training. How do you manage resource allocation and scaling for compute-intensive ML workloads?
Data Processing at Scale
What to coverWhen working with Apache Spark or Databricks, how do you optimize job performance and handle data partitioning? Explain your approach to troubleshooting memory issues or data skew in large-scale pipelines.
ML Model Monitoring and Observability
What to coverHow do you implement monitoring for model drift and performance degradation in production? Detail how you would use tools like Prometheus and Grafana to alert on infrastructure health versus model accuracy.
Data Versioning and Lineage
What to coverDescribe the importance of data lineage in an enterprise ML environment. How would you implement tracking to ensure that a specific model version can be traced back to the exact dataset and code used for training?
Workflow Orchestration
What to coverCompare the use of Airflow versus Kubeflow for managing complex data and ML workflows. In what scenarios would you prefer one over the other for automating data validation and transformation tasks?
Security and Compliance in Data Pipelines
What to coverWhat strategies do you employ to secure sensitive data within automated pipelines? Discuss how you handle secrets management and ensure compliance with data governance standards.
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 performance, scalability, and ease of use?
Troubleshooting Production Incidents
What to coverProvide an example of a critical failure in a data or ML pipeline you managed. Detail the steps you took to identify the root cause, mitigate the impact, and prevent recurrence.
Managing Technical Debt
What to coverTell me about a time you identified an opportunity to improve an existing deployment workflow or infrastructure resilience. How did you justify the effort to stakeholders and implement the changes?
Adapting to New Technologies
What to coverGive an example of how you have stayed current with the rapidly evolving MLOps landscape. How have you applied a new tool or methodology to improve the efficiency of your team's operations?
02
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 ensuring experiment reproducibility. Exercise: Draft a strategy for maintaining model lineage in a production environment using tools like MLflow, ensuring that every model deployment can be traced back to its training data and code version.
Infrastructure as Code (IaC) for Data Platforms
Understand the principles of managing cloud infrastructure (AWS, Azure, or GCP) through code to ensure scalability and resilience. Exercise: Create a Terraform or Ansible script to provision a scalable data processing cluster, focusing on modularity and security best practices for production-grade deployments.
Skills to revise
CI/CD Pipeline Automation
Build automated pipelines for data integration and ML model deployment. Exercise: Design a Jenkins or GitHub Actions workflow that triggers automated data validation tests and model performance checks before deploying a containerized model to a Kubernetes cluster.
Container Orchestration with Kubernetes
Manage containerized applications and ML workloads at scale. Exercise: Configure a Kubernetes deployment manifest for a Python-based ML service, including resource limits, health checks, and horizontal pod autoscaling to handle varying data processing loads.
Data Processing with Apache Spark
Utilize distributed computing frameworks to handle large-scale data transformations. Exercise: Develop a Python script using PySpark to perform a complex data cleaning and validation task, ensuring the process is optimized for execution on a Databricks or Hadoop-based environment.
04
Your preparation checklist
Prepare a technical project walkthrough of an end-to-end MLOps pipeline
Create a summary of a past project where you automated the deployment of an ML model. Focus on how you integrated CI/CD pipelines, managed model versioning, and ensured reproducibility using tools like MLflow or Kubeflow.
Demonstrate Infrastructure as Code (IaC) proficiency
Prepare to discuss how you have used Terraform, CloudFormation, or Ansible to provision cloud infrastructure. Be ready to explain how you handle state management, modularity, and security compliance within your IaC scripts.
Review containerization and orchestration strategies
Be prepared to explain your experience with Docker and Kubernetes in a production environment. Specifically, focus on how you optimize container images for data processing tasks and manage resource allocation for model training and serving.
Articulate your approach to data pipeline monitoring and resilience
Prepare examples of how you have used Prometheus and Grafana to monitor data pipelines. Explain how you set up alerting for model drift, data quality issues, or infrastructure failures to ensure system reliability.
Prepare thoughtful questions for the interviewers, such as: 'How does the team currently balance the hand-off between data scientists and DevOps engineers?' or 'What are the primary challenges the team faces regarding data lineage and governance in your current 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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