Senior Engineer, Machine Learning/AI Ops

National Debt Relief is seeking a Senior Engineer, ML & AI Ops to build and operate its AWS platform for machine learning models, internal AI tools, and event-driven integrations. The role spans ML workflows, secure integrations, infrastructure, and production services, in partnership with Data Science, Data Engineering, Analytics, Product, and other engineering teams.

Responsibilities:

• Design, build, and operate the AWS infrastructure and services behind NDR’s ML models, internal AI tools, and event-driven integrations
• Build and operate event-driven integrations and CDC pipelines between internal and third-party systems, including Salesforce and platforms such as Braze
• Design integrations for reliability and recoverability, including idempotency, replay, schema evolution, and out-of-order events
• Build and maintain containerized inference APIs and backend services with secure, versioned interfaces and production-grade performance
• Manage infrastructure in Terraform across multiple AWS accounts and environments using reusable modules and CI-gated workflows
• Build ML workflows for training, retraining, scoring, and inference, and partner with Data Science to create repeatable paths from experiment to production
• Implement observability across ML systems, AI tools, and integration services, including logging, metrics, alerting, and model monitoring
• Support secure operation of internal LLM and agentic systems, including Amazon Bedrock, with appropriate access controls, auditing, and data protections
• Partner with Data Engineering on Snowflake datasets, data access, and governance supporting ML and operational workflows
• Lead incident response, participate in on-call, and contribute runbooks, reusable patterns, and platform engineering standards

Requirements:

• 6+ years in MLOps, platform engineering, DevOps/SRE, or backend engineering, with significant experience supporting ML, data, or event-driven production workloads
• Bachelor's degree in a relevant technical field, required
• Deep, hands-on AWS experience designing and operating production workloads across compute, messaging, and security
• Strong experience building event-driven integrations with third-party APIs, including change data capture
• Experience with Terraform and infrastructure-as-code across multi-environment production systems
• Hands-on experience deploying and operating ML models, including batch and real-time serving, versioning, and monitoring
• Experience with containers, CI/CD, and safe production deployment practices
• Experience operating PostgreSQL for production services, including schema migrations, indexing, and correctness under concurrent writes
• Strong professional Python experience building and operating production APIs, services, and integrations
• High autonomy and strong communication skills, with the ability to operate from desired outcomes rather than prescribed approaches and influence platform engineering standards
• Computer competency and ability to work with a computer
• Prioritize multiple tasks and projects simultaneously
• Exceptional written and verbal communication skills
• Take constructive feedback
• Experience operating or securing internal LLM-based or agentic systems
• Experience with ML observability, experiment tracking, or drift detection tooling
• Hands-on Snowflake experience supporting ML or production data workloads
• Experience in financial services, fintech, or another regulated industry