Part-Time Data Infrastructure & Quality Lead (AWS/MLOps)

Epam Systems; Ciudad de Mendoza; Hybrid; ARS 181,747,000 - 272,620,000; Part time

Benefits offered by this job

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine is seeking a part-time Data Engineering and Infrastructure Lead to audit AWS architecture, CI/CD processes, and ETL decisions in an advisory capacity. The role weighs managed tooling against custom builds and mentors the team on engineering standards, including SageMaker-based MLOps as appropriate.

You'll evaluate AI-assisted development workflows, provide high-level risk guidance to leadership, and help set technical direction for AWS, DevOps, and data‑engineering practices.

Qualifications

• 6+ years hands-on AWS experience with ECS/Fargate, RDS Postgres, S3, SageMaker, and IAM.
• Fluency with Infrastructure-as-code using Terraform.
• Proven track record auditing CI/CD pipelines and SDLC governance.

Responsibilities

• Audit AWS architecture across live applications, including ECS/ECR, RDS, S3, VPC, CloudFront/SSO, and SageMaker MLOps.
• Review CI/CD process and SDLC governance, recommending environment separation and release gating.
• Evaluate Claude Code usage for code generation and reviews to meet engineering standards.
• Advise on ETL/data-ingestion architecture: managed tools vs. custom builds.
• Mentor the infrastructure build team in AWS, DevOps, CI/CD, and data engineering practices.
• Set recommendations and technical direction based on audit findings.
• Report findings to leadership with risk assessment.

Skills

AWS expertise

Infrastructure as code

CI/CD governance

Mentorship

Communication

Tools

Terraform

Claude Code

Alembic

ECS/Fargate

Job description

AgileEngine is seeking a part-time Data Engineering and Infrastructure Lead to audit AWS architecture, CI/CD processes, and ETL decisions in an advisory capacity. The role weighs managed tooling against custom builds and mentors the team on engineering standards, including SageMaker-based MLOps as appropriate.

You'll evaluate AI-assisted development workflows, provide high-level risk guidance to leadership, and help set technical direction for AWS, DevOps, and data‑engineering practices.