Senior ML Engineer, MLOps — Production Pipelines

ExaCare AI; New York (NY); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible PTO

Comprehensive benefits (medical, dent,

Equity

Company off-sites

Job summary

exacare ai is seeking a Senior Machine Learning Engineer focused on MLOps to operationalize and scale ML systems. This engineering-focused role emphasizes building workflows, infrastructure, and processes to move ML from research into reliable production.

You will partner with research teams to productionize models, design and improve data/training pipelines, and optimize deployment, monitoring, and infrastructure for speed, reliability, and cost-efficiency.

Qualifications

• Proven experience in ML engineering, MLOps, and production ML systems.
• Experience building end-to-end ML pipelines and production workflows.
• Strong software engineering fundamentals and production-grade systems.

Responsibilities

• Build and maintain end-to-end ML workflows and infrastructure.
• Partner with researchers to productionize models for faster iteration.
• Design and improve data pipelines and training pipelines.
• Improve data processing, annotation workflows, and ML system efficiency.
• Deploy and maintain background systems for training and inference.
• Build tooling for monitoring model performance and system health.
• Improve scalability, observability, and reproducibility of ML systems.
• Optimize ML infrastructure for speed, reliability, and cost-efficiency.
• Identify bottlenecks and automate manual processes.
• Establish best practices around ML operations, deployment, and performance.

Skills

ML Engineering

MLOps

ML Infrastructure

Data Engineering

Backend/Platform

Job description

exacare ai is seeking a Senior Machine Learning Engineer focused on MLOps to operationalize and scale ML systems. This engineering-focused role emphasizes building workflows, infrastructure, and processes to move ML from research into reliable production.

You will partner with research teams to productionize models, design and improve data/training pipelines, and optimize deployment, monitoring, and infrastructure for speed, reliability, and cost-efficiency.