Senior ML Operations Engineer II - Monetization

Fetch•US - Remote,

About The Position

The Senior ML Operations Engineer II will own and improve the complete production model lifecycle, from experimentation and validation through deployment, versioning, retraining, rollback, and retirement. The role establishes model promotion criteria and release processes, and builds monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost. The engineer partners with data scientists to define offline and production evaluation signals, including for use cases with delayed labels, and uses production evidence to improve model performance, platform reliability, deployment speed, and infrastructure efficiency. The role builds reusable training, evaluation, deployment, and orchestration infrastructure; translates scientific requirements into maintainable production systems; and maintains an accessible model hub with documentation, evaluation results, release history, versioning, and monitoring. The position also creates tested CI/CD workflows, reproducible environments, alerts, incident-response procedures, and runbooks. In partnership with Data Science and Engineering, the engineer defines MLOps priorities, identifies risks, establishes reusable standards, mentors teammates, and reduces the operational burden on data scientists.

Requirements

5+ years managing production ML systems beyond initial deployment, including monitoring, retraining, incident response, and continuous improvement.
Strong production software development skills in Python and proficiency with SQL.
Hands-on experience with Spark for distributed data processing.
Experience with a workflow orchestration engine for scheduling, dependency management, retries, backfills, and recovery.
Hands-on experience building infrastructure with Terraform.
Experience deploying and operating containerized production workloads on Kubernetes.
Experience building reusable training, evaluation, or deployment infrastructure used by multiple models, scientists, or teams.
Experience implementing model and dataset versioning, experiment tracking, lineage, and reproducible releases.
Ability to distinguish service or infrastructure failures from changes in model effectiveness.
Experience building CI/CD pipelines, monitoring, alerting, rollback mechanisms, and recovery procedures for production ML systems.
Demonstrated ability to balance model quality, delivery speed, reliability, and infrastructure cost.
Strong cross-functional communication and experience partnering with data scientists to preserve scientific validity as models move into production.
Ability to explain a production incident or model degradation issue, how it was detected, and what improvements followed.

Nice To Haves

Experience developing platform services, infrastructure, or internal tooling in Go.
Experience building or owning a model registry, model hub, or similar system used by technical and nontechnical stakeholders.
Experience supporting ML systems where outcome labels are delayed or model quality cannot be measured immediately.
Track record of improving deployment speed, model quality, system reliability, infrastructure cost, or manual operational effort.
Experience designing configurable ML infrastructure that supports several modeling frameworks or approaches.
Evidence that reusable infrastructure, standards, or documentation were adopted across multiple teams.
Experience mentoring engineers or data scientists and influencing technical priorities across an ML portfolio.
Experience working with sensitive information and designing documentation or monitoring around appropriate access boundaries.

Responsibilities

Own the complete model lifecycle, from experimentation and validation through deployment, versioning, retraining, rollback, and retirement.
Establish model promotion criteria, release processes, and rollback paths.
Build monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost.
Partner with data scientists to define offline and production evaluation signals, including for use cases with delayed outcome labels.
Use production evidence to improve model performance, platform reliability, deployment speed, and infrastructure efficiency.
Build reusable training, evaluation, and deployment pipelines for multiple models and modeling approaches.
Develop orchestration workflows covering dependencies, scheduling, retries, backfills, and failure recovery.
Translate scientific requirements into maintainable production systems with clear interfaces, ownership, and operational expectations.
Build and maintain an accessible model hub connecting model documentation, evaluation results, release history, versioning, and monitoring.
Create tested CI/CD workflows, reproducible environments, actionable alerts, incident-response procedures, and operational runbooks.
Define MLOps priorities with Data Science and Engineering and identify risks before they become production issues.
Establish reusable engineering standards, mentor teammates, and reduce the operational burden on data scientists.