ML Systems Engineer – Production AI Infrastructure

Bask Health; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Bask Health seeks an ML Systems Engineer to design and maintain production infrastructure enabling machine learning models and AI-powered services to operate reliably across our platform. You will support data preparation, deployment, monitoring, and ongoing maintenance while coordinating with ML engineers, scientists, and backend teams.

The role combines backend software development, cloud infrastructure, MLOps practices, and distributed systems to deliver scalable, secure, and observable ML

Qualifications

• 3+ years of professional experience in software engineering, machine learning engineering, infrastructure engineering, or a closely related technical field.
• Experience building and maintaining production backend services or infrastructure.
• Strong programming skills in Python, TypeScript, or comparable languages.
• Understanding of machine learning development and deployment workflows.
• Experience developing APIs and integrating backend services.
• Familiarity with cloud infrastructure and production application deployment.

Responsibilities

• Design, build, and maintain production infrastructure for machine learning systems across Bask Health.
• Develop model-serving services that expose machine learning predictions through reliable APIs.
• Build and maintain infrastructure for batch and real-time model inference.
• Implement automated workflows for model packaging, deployment, and version management.
• Develop reusable deployment patterns for machine learning applications.
• Build systems for managing model artifacts, configurations, and runtime dependencies.
• Implement continuous integration and deployment pipelines for ML services.
• Develop infrastructure that supports reproducible model releases and controlled rollbacks.
• Build monitoring systems for model-serving latency, throughput, errors, and availability.
• Implement logging and tracing for production machine learning services.
• Support model performance monitoring and detection of unexpected changes in input data.
• Develop data processing and feature preparation pipelines that support production inference.
• Integrate machine learning services with existing backend applications and databases.
• Build asynchronous processing systems for large or long-running inference tasks.
• Optimize inference performance, resource usage, and infrastructure costs.
• Develop automated tests for model-serving APIs and infrastructure components.
• Implement safeguards for service failures, timeouts, retries, and fallback behavior.
• Work with machine learning engineers to define deployment and operational requirements.
• Collaborate with applied scientists to transition validated models into production.
• Develop tools that simplify model deployment and troubleshooting for engineering teams.
• Implement appropriate authentication, authorization, and access controls for ML services.
• Ensure production systems respect tenant boundaries and approved data-handling requirements.
• Investigate infrastructure incidents and resolve production reliability issues.
• Improve system scalability and fault tolerance as application demand grows.
• Support experiment environments and testing infrastructure for machine learning development.
• Document infrastructure architecture, deployment procedures, and operational standards.
• Participate in code reviews, technical planning, and architecture discussions.
• Own assigned ML infrastructure projects from design through deployment, monitoring and continuous improvement.

Skills

Python

TypeScript

APIs

Cloud infrastructure

Distributed systems

CI/CD

Logging/monitoring

Troubleshooting

Git workflows

Tools

Docker

Kubernetes

AWS

Terraform

Job description

Bask Health seeks an ML Systems Engineer to design and maintain production infrastructure enabling machine learning models and AI-powered services to operate reliably across our platform. You will support data preparation, deployment, monitoring, and ongoing maintenance while coordinating with ML engineers, scientists, and backend teams.

The role combines backend software development, cloud infrastructure, MLOps practices, and distributed systems to deliver scalable, secure, and observable ML