New opportunity

Senior Systems Engineer - Data DevOps/MLOps

EPAM Systems · Pune, 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 building automated data pipelines and ensuring the seamless deployment of ML models into production environments. Responsibilities include managing CI/CD pipelines for data integration and model rollout, maintaining cloud-based infrastructure for processing and training, and streamlining data validation and workflow orchestration. You will collaborate with data scientists and software engineers to improve model serving, monitoring, and reliability while ensuring data versioning, lineage tracking, and security compliance. 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 be proficient in cloud platforms (Azure, AWS, or GCP), Infrastructure as Code (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, along with expertise in CI/CD tools (Jenkins, GitLab, GitHub Actions) and MLOps platforms (MLflow, Kubeflow). Monitoring experience with Prometheus and Grafana is essential. Preferred qualifications include experience with DataOps principles (Airflow, 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 DevOpsMLOpsMLflowKubeflowCI/CDInfrastructure as CodeDockerKubernetesApache SparkDatabricksPythonPandasTensorFlowPyTorchJenkinsGitGitHub ActionsTerraformAnsiblePrometheusGrafanaAirflowdbtHadoopHive

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

Architecting and Automating ML Pipelines
The core responsibility is to build and maintain end-to-end CI/CD pipelines for data integration and model deployment. You must ensure that ML models move from development to production seamlessly, focusing on reproducibility, versioning, and lineage tracking.
Infrastructure Management and Scalability
You are expected to manage cloud-based infrastructure for data processing and model training using Infrastructure as Code (IaC) tools. This requires maintaining resilient, scalable environments using containerization (Docker/Kubernetes) and cloud platforms like AWS, Azure, or GCP.
Cross-Functional Collaboration and Reliability
You will partner with data scientists and software engineers to improve model serving and monitoring. A key priority is ensuring production reliability through robust logging, alerting, and performance monitoring using tools like Prometheus and Grafana.

Likely challenges to think through

Balancing Speed with Data Governance
The role requires enforcing security and regulatory compliance while streamlining automated workflows. You may face friction when implementing strict data lineage and governance controls that could potentially slow down rapid experimentation cycles.
Managing Complexity in Heterogeneous Environments
Supporting large-scale pipelines across various cloud providers and frameworks (Spark, Databricks, Kubeflow) creates significant technical complexity. You must be prepared to diagnose and resolve issues across the entire lifecycle, which requires deep expertise in both infrastructure and data science tooling.

Practice your answers

Technical practice

Designing Scalable MLOps Pipelines

What to coverExplain how you would architect an end-to-end MLOps pipeline that ensures reproducibility from data ingestion to model deployment. Focus on how you integrate version control for both code and data, and how tools like MLflow or Kubeflow facilitate experiment tracking.

Infrastructure as Code (IaC) Strategy

What to coverDescribe your approach to managing cloud infrastructure using Terraform or Ansible. Discuss how you ensure infrastructure consistency across development, staging, and production environments while maintaining security compliance.

Container Orchestration in ML Workflows

What to coverHow do you leverage Kubernetes to manage the lifecycle of ML models? Address challenges related to resource allocation, scaling training jobs, and ensuring high availability for model serving endpoints.

CI/CD for Data and ML

What to coverWhat are the unique challenges of implementing CI/CD for machine learning compared to traditional software? Explain how you handle automated testing for data quality, model validation, and the rollout of new model versions.

Monitoring and Reliability

What to coverUsing Prometheus and Grafana, how would you set up monitoring for a production ML model? Explain the difference between monitoring system metrics (CPU/Memory) and model-specific metrics like data drift or prediction accuracy.

Data Processing at Scale

What to coverCompare the use of Apache Spark and Databricks for large-scale data transformation. Discuss scenarios where you would choose one over the other and how you integrate these frameworks into an automated workflow.

Data Lineage and Governance

What to coverWhy is data lineage critical in an enterprise ML environment? Discuss how you ensure traceability of data from source to model output and how you might implement governance practices to meet regulatory requirements.

Python for Data Engineering

What to coverDescribe how you use Python libraries like Pandas for data validation and preprocessing within a pipeline. Suggestion: Provide an example of how you handle large datasets that exceed memory limits during transformation.

Behavioural practice

Cross-functional Collaboration

What to coverDescribe a situation where you had to bridge the gap between data scientists and software engineers to resolve a deployment issue. Focus on how you communicated technical constraints and reached a consensus on the solution.

Troubleshooting Complex Pipeline Failures

What to coverTell me about a time you had to diagnose a critical failure in a production data pipeline. What steps did you take to isolate the root cause, and what measures did you implement to prevent recurrence?

Managing Technical Debt in MLOps

What to coverGive an example of a time you identified an opportunity to improve an existing deployment workflow or infrastructure resilience. How did you prioritize this improvement against ongoing project deliverables?

Adapting to New Technologies

What to coverDescribe a project where you had to quickly learn and implement a new cloud service or MLOps tool to meet a business requirement. How did you ensure the implementation was robust and well-documented?

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: Draft a strategy for maintaining model lineage in a production environment using MLflow, ensuring that every model deployment can be traced back to the specific training data and code version used.
Infrastructure as Code (IaC) for Data Pipelines
Understand the principles of managing cloud infrastructure through code to ensure scalability and resilience. Exercise: Design a Terraform or Ansible script that provisions a scalable cluster for data processing, incorporating best practices for security compliance and environment consistency.
CI/CD for Data and ML Workflows
Study the integration of automated testing and deployment for data pipelines and ML models. Exercise: Create a conceptual CI/CD pipeline diagram using GitHub Actions or Jenkins that includes automated data validation steps and model performance testing before production rollout.

Skills to revise

Container Orchestration with Kubernetes
Develop proficiency in deploying and managing containerized ML workloads. Exercise: Create a Kubernetes manifest file to deploy a Python-based model serving application, including resource limits and health checks to ensure high availability.
Monitoring and Alerting with Prometheus and Grafana
Gain hands-on experience in observing system and model performance. Exercise: Configure a basic Prometheus exporter to track custom metrics from a Python application and visualize these metrics in a Grafana dashboard to monitor model latency and data drift.
Data Processing with Apache Spark
Practice large-scale data manipulation and transformation. Exercise: Write a PySpark script to perform a complex data transformation task, such as joining large datasets and handling missing values, which is essential for preparing data for ML model training.

Your preparation checklist

Architect an end-to-end MLOps pipeline walkthrough
Prepare a technical summary of a project where you automated the deployment of an ML model. Focus on how you integrated CI/CD tools like Jenkins or GitHub Actions with MLOps platforms like MLflow or Kubeflow to ensure reproducibility and versioning.
Demonstrate Infrastructure as Code (IaC) proficiency
Be ready to explain how you have used Terraform, Ansible, or CloudFormation to provision and manage cloud infrastructure (AWS, Azure, or GCP) for data processing. Provide examples of how you ensured scalability and security compliance in your infrastructure design.
Prepare a case study on monitoring and reliability
Detail a specific instance where you implemented monitoring and alerting using Prometheus and Grafana. Explain how you diagnosed and resolved performance bottlenecks or data drift issues within a production data pipeline.
Formulate questions regarding EPAM's data ecosystem
Prepare thoughtful questions about their current stack, such as how they handle data lineage and governance with tools like Collibra, or how they manage workflow orchestration using Airflow or dbt in their specific 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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