[Hiring] Senior DATA & AI Architect Engineer @EPAM Systems

Role Description

Every platform that supports machine learning and artificial intelligence at scale faces a unique set of architectural challenges. The Senior DATA & AI Architect role in Portugal is built for those who turn technical intent into practical guidance that delivery teams can apply and validate on live products. In this position, you shape the standards and patterns that make complex ML systems reliable, governable and ready for production. You work directly with architects and subject matter experts to ensure every decision is grounded in real-world needs. The person in this role sets the direction for how advanced data and AI capabilities are delivered and maintained.

• Define the machine learning development and deployment approach, including assessment of current implementation and available platform capabilities.
• Define the end-to-end MLOps approach: data and feature preparation, experimentation, training, validation, model registration, deployment, monitoring, retraining and rollback.
• Cover source control, CI/CD, environment separation and operational ownership.
• Define batch scoring and/or online serving, and how predictions reach downstream consumers (Power BI, applications, APIs, MCP).
• Establish the delivery approach and onboarding Ways of Working using platform standards, layering conventions and release processes, building on existing documentation.
• Document interoperability and integration patterns between key data platforms.
• Rationalise and publish platform documentation, improve site structure and consistency, and align with existing guidance.
• Translate approved architecture into implementation guidance, including standards, reference patterns, decision guides and playbooks.

Qualifications

• Proven experience in modern data platform architecture or technical consulting, with hands-on delivery in machine learning and AI environments.
• Strong practical MLOps experience, including tracking, model registry, model serving and/or batch scoring, model monitoring and retraining.
• CI/CD and environment promotion for data and ML workloads.
• Working knowledge of data engineering, architecture governance, data governance and data quality.
• Experience translating architecture into implementation standards, playbooks and engineering guidance.

Requirements

• Experience with additional data platforms and tools.
• Exposure to advanced operations for large language models or agent-based systems.
• Background in regulated industries such as life sciences or internal IT audit.