Senior ML & AI Ops Engineer - Remote AWS Platform

National Debt Relief; Idaho; Remote; USD 151,000 - 173,000; Full time

Benefits offered by this job

Medical, dental, and vision benefits

401(k) with company match

Paid holidays and vacation

Paid parental leave

Job summary

National Debt Relief is seeking a Senior Engineer, ML & AI Ops to design, build, and operate the AWS platform supporting our ML models, internal AI tools, and event-driven integrations.

The role focuses on production ML workflows, secure integrations, observability, and scalable services across multi-account AWS environments, working closely with data science, data engineering, and product teams.

Qualifications

• 6+ years in MLOps, platform engineering, DevOps/SRE, or backend engineering with ML/data/event-driven workloads.
• Bachelor’s degree in a relevant technical field.
• Deep AWS experience designing and operating production workloads across compute, messaging, and security.
• Experience with Terraform and infrastructure-as-code across multi-environment production systems.
• Hands-on experience deploying and operating ML models with batch and real-time serving, versioning, and monitoring.

Responsibilities

• Design, build, and operate the AWS infrastructure and services behind NDR’s ML models and AI tools.
• Build event-driven integrations and CDC pipelines with Salesforce and Braze.
• Develop reliable, secure, versioned interfaces for containerized inference APIs and services.
• Manage Terraform across AWS accounts with reusable modules and CI-gated workflows.
• Lead incident response and contribute runbooks and platform engineering standards.

Skills

Python

Autonomy & communication

Education

Bachelor’s degree in a technical field

Tools

Terraform

Snowflake

PostgreSQL

CI/CD

AWS

Job description

National Debt Relief is seeking a Senior Engineer, ML & AI Ops to design, build, and operate the AWS platform supporting our ML models, internal AI tools, and event-driven integrations.

The role focuses on production ML workflows, secure integrations, observability, and scalable services across multi-account AWS environments, working closely with data science, data engineering, and product teams.