The posting
About the Role Fetch is built on real purchase data shared by millions of users scanning their receipts. Data Science turns that signal into predictive models that generate valuable user intelligence for our stakeholders and partners. Those models need a reliable operational foundation to move confidently from experimentation into production and continuously improve over time.
As a Senior MLOps Engineer, you’ll own the systems and practices that make Fetch’s models production-ready, observable, reproducible, and resilient. You’ll partner closely with Data Science and Engineering across the full model lifecycle, including training, validation, deployment, versioning, monitoring, retraining, and retirement.
This is a hands-on technical leadership role. You’ll build reusable infrastructure, establish standards for model health and production reliability, and create a shared model hub that makes each model’s purpose, status, evaluation results, release history, and performance accessible. Your work will help teams identify issues earlier, improve models faster, and support every production claim with clear evidence.
Role Responsibilities Own the complete model lifecycle from experimentation through validation, deployment, versioning, retraining, rollback, and retirement. Establish clear model promotion criteria, release processes, and rollback paths that enable teams to ship improvements confidently. Build monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost. Partner with data scientists to define meaningful 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 that support multiple models and modeling approaches. Develop dependable 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 in partnership with Data Science and Engineering, identifying risks before they become production issues. Establish reusable engineering standards, mentor teammates, and reduce the operational burden placed on data scientists.
Minimum 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 using 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.
Preferred Requirements 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 in environments with sensitive information and designing documentation or monitoring around appropriate access boundaries.
