Senior MLOps Engineer: Build Real-Time ML Platform, Hybrid

SmartRecruiters, Inc.; King of Prussia (PA); Hybrid; USD 165,000 - 175,000; Full time

Benefits offered by this job

Equity

Hybrid work model

Fully paid medical, dental, and vision

Life and AD&D insurance

Unlimited PTO

Job summary

SmartRecruiters, Inc. is seeking a Senior MLOps Engineer to own the ML platform from training through production deployment and monitoring. You will build systems that operate at real time, support scalable data pipelines, and work closely with ML engineers to operationalize models in production.

The role emphasizes building an ML stack from the ground up, with ownership over deployment, cost management, and architecture decisions in a fast-growing SaaS startup.

Qualifications

• 5-7+ years of professional software engineering or data engineering experience.
• 2+ years building and operating ML systems in production.
• Hands-on experience building MLOps infrastructure from the ground up.
• Strong backend experience with production Python services.
• Experience scaling production data pipelines and managing costs.

Responsibilities

• Own and extend the ML platform end to end, from training to production monitoring.
• Build and operate data pipelines supporting model training and online inference.
• Build backend services and APIs for ML models in production.
• Design processes for backfills, replays, and data recovery.
• Ensure training data reflects what was known at the point in time and reproducibility.
• Monitor model performance over time and manage costs.
• Contribute to technical architecture and reviews.

Skills

MLOps

Production ML

Python backend

Data pipelines

Deployment & monitoring

Model training orchestration

Experiment tracking

Entrepreneurial mindset

Education

Bachelor's degree in Computer Science or related field

Tools

Kubernetes

PostgreSQL

Docker

Model Registry

Airflow

Job description

SmartRecruiters, Inc. is seeking a Senior MLOps Engineer to own the ML platform from training through production deployment and monitoring. You will build systems that operate at real time, support scalable data pipelines, and work closely with ML engineers to operationalize models in production.

The role emphasizes building an ML stack from the ground up, with ownership over deployment, cost management, and architecture decisions in a fast-growing SaaS startup.