Senior DATA & AI Architect Engineer

EPAM Systems; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

EPAM Systems Inc. is seeking a senior ML Platform Architect to drive end-to-end MLOps and data platform integration across enterprise AI deployments.

You will shape the ML lifecycle from data prep to retraining, aligning with platform standards and governance. Responsibilities include defining deployment strategies, model serving, and batch scoring, plus creating practical guidance, playbooks, and reference patterns for engineering teams.

Qualifications

• Experience delivering ML/AI workloads in modern data platforms.
• Hands-on MLOps including model registry, serving, and retraining CI/CD.
• Strong ability to translate architecture into implementation standards and playbooks.
• Exposure to regulated industries such as life sciences or internal IT audit.

Responsibilities

• Define the machine learning development and deployment approach, including assessment of current implementation and platform capabilities.
• Define the end-to-end MLOps approach: data and feature prep, 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.

Skills

Data platform architecture

ML/AI environments

MLOps

Model registry

Model serving

Batch scoring

Data engineering

Architecture governance

Data governance

Data quality

Playbooks

Engineering guidance

Life sciences

Job description

Responsibilities

• 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

Requirements

• 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