Machine Learning Operations Engineer

Own the reliability and performance of ML model inference systems in production

Ensure high availability of deployed models across APIs and enterprise products

Build systems to handle scaling, load variability, and production traffic growth

Reduce operational burden through better tooling, automation, and processes

Help define how Modulate runs ML systems at scale with reliability and efficiency

Deploy, monitor, and maintain production machine learning inference systems

Oversee fleets of inference machines and ensure system health and performance

Design monitoring, alerting, and incident response systems for ML workloads

Participate in on-call rotations and lead incident response and debugging

Build systems and processes for scaling inference infrastructure under variable load

Improve reliability and observability of production ML services

Collaborate on infrastructure-as-code for production deployments

Support or contribute to GPU-based training and inference infrastructure

Work closely with ML and engineering teams to ensure smooth model deployments

(Optional growth area) Optimize model inference performance and latency

Experience deploying and maintaining production software systems

Experience building monitoring and alerting systems for production environments

Experience with on-call rotations and incident response

Strong experience with AWS, Python, and Linux

Exposure to PyTorch or similar ML frameworks

Experience working with GPU-based applications and basic GPU tooling (drivers, runtime, monitoring)

Strong debugging and systems thinking skills

Ability to operate calmly in production incident environments

Experience with ML model serving systems or dedicated model servers

Experience monitoring GPU performance for inference workloads

Experience optimizing machine learning model inference

Familiarity with audio or multimedia data (codecs, streaming, real-time systems)

Experience with infrastructure-as-code (e.g., Terraform, CloudFormation)