Jobs in Portugal, in English.

Senior DATA & AI Architect Engineer

Remote in Portugal·Added yesterday (last checked 11 Nov)

EPAM

64 open roles

Apply

Our summary

Work mode: Remote (Portugal)
Experience: Senior level

Skills

Requirements

What we're looking for

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

Nice to have

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

Pay & benefits

What you'll get

Competitive compensation depending on experience and skills
Variety of projects within one company
Being a part of a project following engineering excellence standards
Individual career path and professional growth opportunities
Internal events and communities
Flexible work hours

The role

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.

What you'll do

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

Hiring process

3 steps3 interviews

Apply
Conversation with Talent Acquisition
With a Talent Acquisition Specialist
An introductory conversation with a Talent Acquisition Specialist.
Technical interview
Video call
An online technical interview.
Interview with the hiring manager
With a hiring manager
A general interview with the hiring manager.
Offer