Senior Systems Engineer - Data DevOps/MLOps

What matters most

• End-to-End MLOps Pipeline Automation

  The core requirement is to design and implement robust CI/CD pipelines that bridge the gap between data integration and production ML model deployment. You must demonstrate the ability to automate complex workflows, including data validation, transformation, and orchestration, ensuring that models move from experimentation to live environments seamlessly.

• Scalable Cloud Infrastructure Management

  You are expected to deploy and maintain cloud-based infrastructure (AWS, Azure, or GCP) using Infrastructure as Code (IaC) tools like Terraform or Ansible. This involves optimizing infrastructure for data processing and model training while ensuring resilience, security compliance, and cost-effective performance.

• Cross-Functional Collaboration and Model Lifecycle Governance

  Success in this role requires working closely with data scientists and software engineers to maintain data versioning, lineage, and reproducibility. You must be able to translate technical ML requirements into reliable production systems while maintaining documentation and monitoring standards.

Likely challenges to think through

• Balancing Model Reproducibility with Rapid Deployment

  A common challenge in MLOps is maintaining strict data lineage and model versioning without slowing down the deployment velocity. Prepare to discuss how you use tools like MLflow or Kubeflow to ensure that every production model is traceable and reproducible, even when dealing with frequent updates or complex data dependencies.

• Troubleshooting Complex Pipeline Failures

  The role involves resolving issues across the entire lifecycle, from data ingestion to model serving. You should be prepared to explain your methodology for debugging distributed systems, such as identifying bottlenecks in Spark/Databricks jobs or diagnosing container orchestration issues in Kubernetes that impact model reliability.

Topics to prepare

• MLOps Lifecycle and Reproducibility

  Focus on the end-to-end management of ML models, specifically data versioning, lineage tracking, and model reproducibility. Exercise: Design a conceptual architecture for tracking model experiments and data versions using MLflow, ensuring that every production model can be traced back to its training data and code version.

• CI/CD for Data and ML Pipelines

  Understand the integration of automated testing, validation, and deployment for data pipelines and ML models. Exercise: Outline a CI/CD pipeline strategy that includes automated data quality checks and model performance validation before deployment to a production environment.

• Infrastructure as Code (IaC) and Cloud Resilience

  Master the deployment of scalable cloud infrastructure using IaC tools. Exercise: Create a Terraform script or configuration template to provision a resilient, auto-scaling Kubernetes cluster on a cloud provider (AWS, Azure, or GCP) suitable for hosting ML model training workloads.

Skills to revise

• Containerization and Orchestration

  Demonstrate proficiency in Docker and Kubernetes for deploying ML services. Exercise: Build a Dockerfile for a Python-based ML model inference service and write a Kubernetes deployment manifest to manage its scaling and availability.

• Data Processing with Spark or Databricks

  Apply big data processing frameworks to automate data transformation workflows. Exercise: Develop a PySpark script to perform data cleaning and feature engineering on a large dataset, ensuring the process is optimized for execution within a Databricks or Spark environment.

• Monitoring and Observability

  Implement monitoring for production ML systems using Prometheus and Grafana. Exercise: Configure a dashboard in Grafana that tracks key performance indicators for a deployed model, such as inference latency, error rates, and resource utilization.

• Architect an end-to-end MLOps pipeline walkthrough

  Prepare a technical summary of a past project where you automated the deployment of an ML model. Focus on the tools used for CI/CD (e.g., Jenkins or GitHub Actions), containerization (Docker/Kubernetes), and how you ensured reproducibility using platforms like MLflow or Kubeflow.

• Demonstrate Infrastructure as Code (IaC) proficiency

  Create a sample Terraform or Ansible script that provisions a cloud-based environment (AWS, Azure, or GCP) for data processing. Be ready to explain how you manage state, handle security compliance, and ensure infrastructure resilience in your code.

• Prepare a case study on pipeline troubleshooting

  Identify a specific challenge you faced in a data or ML pipeline lifecycle, such as model drift, data quality issues, or deployment failures. Detail your diagnostic process using monitoring tools like Prometheus or Grafana and the steps taken to resolve the issue.

• Review data engineering and orchestration fundamentals

  Refresh your knowledge on Apache Spark or Databricks for large-scale data processing. If you have experience with Airflow or dbt, prepare to discuss how these tools integrate into a broader DataOps strategy to improve workflow orchestration.

• Formulate strategic questions for the interviewers

  Prepare insightful questions regarding EPAM's current MLOps maturity, such as how they handle data lineage and governance in production, the specific cloud challenges their teams are currently solving, or how they balance model performance with infrastructure costs.