Senior Machine Learning Ops Engineer

National Debt Relief, LLC.•,

5d•$150,500 - $173,000•Remote

About The Position

National Debt Relief (NDR) is seeking a Senior MLOps Engineer to help evolve and scale our enterprise machine learning platform. This role sits within the Data Platform organization on the newly formed MLOps team and partners closely with Data Science, Data Engineering, and Applied AI teams to productionize data & machine learning workloads across the company. You will help own the infrastructure, orchestration, deployment, observability, and reliability of business critical data & ML pipelines. This includes enabling scalable model training and inference workflows, improving developer experience for Data Science teams, and establishing engineering standards for testing, CI/CD, governance, and monitoring. The ideal candidate combines strong software engineering fundamentals with hands-on Ops experience across cloud infrastructure, orchestration, containerization, and data systems.

Requirements

Bachelor's degree in Computer Science, Data Engineering, or a related field (advanced degree preferred).
6 years of experience in ML Ops, platform engineering, DevOps, or data platform engineering.
3+ years of hands-on experience with AWS cloud infrastructure.
Experience managing Infrastructure-as-Code projects using tooling such as Terraform, OpenTofu, or CloudFormation.
Strong SQL expertise and hands-on experience with a modern data warehouse platform like Databricks, Snowflake, or BigQuery.
Experience implementing CI/CD workflows and modern software engineering best practices.
Experience with orchestration frameworks such as Dagster, Airflow, or Prefect.
Experience with pytest testing frameworks and patterns, including unit, integration, and end-to-end testing.
Experience with Bash and Unix-based environments.
Strong Python engineering skills, including API development and automation tooling.
Strong experience deploying and operating production machine learning systems.
Strong experience with Docker and containerized application deployment.
Demonstrated experience building backend services using frameworks such as FastAPI, Flask, or Django.
Strong software engineering fundamentals, including design patterns and maintainable architecture practices.
Experience deploying ML systems on Kubernetes, ECS, EKS, or other container orchestration platforms.
Strong communication and collaboration skills across Data Science, Data Engineering, and Product teams.
Ability to operate independently and help define ML platform standards and architecture direction.
Computer competency and ability to work with a computer.
Prioritize multiple tasks and projects simultaneously.
Exceptional written and verbal communication skills.
Punctuality expected, ready to report to work on a consistent basis.
Attain and maintain high performance expectations on a monthly basis.
Work in a fast-paced, high-volume setting.
Use and navigate multiple computer systems with exceptional multi-tasking skills.
Remain calm and professional during difficult discussions.
Take constructive feedback.
Available for full-time position.

Nice To Haves

Experience with ML observability and experiment tracking tools such as MLflow, Arize, Evidently, WhyLabs, or Monte Carlo.
Experience designing feature stores or reusable ML data products.
Experience supporting both batch and low-latency inference workloads.
Experience in financial services, fintech, or other regulated industries.
Experience supporting Generative AI or LLM deployment workflows.

Responsibilities

Design, deploy, and maintain scalable ML infrastructure supporting model training, batch inference, and real-time inference workloads.
Own the provisioning and deployment of infrastructure and services across AWS, Snowflake, and other related platforms using Infrastructure-as-Code.
Build and maintain containerized model serving solutions using Docker, FastAPI, and modern deployment patterns.
Document architecture, deployment standards, and operational processes to support maintainability and reproducibility.
Partner closely with Data Science and Data Engineering teams to productionize ML models and improve deployment velocity.
Implement CI/CD, Infrastructure-as-Code, testing, and deployment automation best practices across ML systems and platform infrastructure.
Establish observability and monitoring frameworks for deployed ML systems, including model performance monitoring, drift detection, data quality validation, and automated alerting.
Optimize platform reliability, scalability, governance, and operational efficiency across ML workflows and supporting infrastructure.