Senior AWS DevOps Engineer

Virtusa helps its Clients by becoming a true extension of their software and data development capabilities. Through the readily set up, comprehensive, and self-governing teams, we let our Clients focus on their business while we make sure that their software products and data tools scale up accordingly and with outstanding quality. We are looking for experienced team players to fill the position of Senior DevOps Engineer and participate in our up-and-coming project for our client from the fintech area. This is a fully remote project.

• Strong proficiency in Amazon Web Services (AWS) ecosystem.
• Practical knowledge or strong interest in Google Cloud Platform (GCP) is a significant plus.
• Hands-on experience with Infrastructure as Code tools (e.g., Terraform, CloudFormation) applied to data warehouses like Snowflake and cloud services.
• Deep understanding of Apache Airflow infrastructure management.
• Experience or strong interest in modern workflow engines (e.g., Prefect, Dagster, Temporal).
• Proven experience in configuring CI/CD pipelines and managing dedicated runner setups (e.g., GitLab Runners, GitHub Actions, Jenkins).
• Familiarity with provisioning infrastructure for Data Science workflows, ML pipelines, and AI systems (specifically working with MCP servers or LLM infrastructure).
• Foundational knowledge of modern cloud data platforms (Snowflake, and ideally BigQuery).

Data Platform & Cloud Refactoring:

• Architect and refactor our existing core AWS data platform setup to ensure maximum scalability, security, high availability, and cost-efficiency.

Data Orchestration Management:

• Maintain and refactor the infrastructure supporting Apache Airflow.
• Plan, set up, and provision infrastructure for a next-generation workflow engine destined to replace Airflow.

CI/CD Optimization:

• Design, configure, and maintain robust CI/CD runner setups to optimize deployment speeds and ensure seamless code integration across all environments.

Advanced Analytics & AI Infrastructure:

• Build and scale scalable infrastructure dedicated to running Data Science pipelines and AI systems, with a particular focus on Model Context Protocol (MCP) servers.