Senior MLOps Engineer — Scale ML in Retail Ops

Forsyth Barnes; United States; Hybrid; USD 150,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Paid time off

Parental leave

Professional development budget

401(k) retirement plan

Job summary

Forsyth Barnes is seeking a Senior MLOps Engineer to own end-to-end ML pipelines in a high-volume retail tech environment. You will collaborate with data scientists and software engineers to operationalise models from data ingestion to deployment and monitoring, aligning with business KPIs such as conversion and shrink.

The role focuses on scalable infrastructure, model versioning, experiment tracking, and cost-optimised inference, with a strong emphasis on governance, reliability and

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science or equivalent practical experience.
• 5+ years of experience deploying and operating production ML systems and MLOps pipelines.
• Strong programming proficiency in Python and experience with Docker and Kubernetes.
• Experience with CI/CD tooling, infrastructure-as-code and automated deployment strategies.
• Familiarity with monitoring and observability platforms for ML and techniques for detecting data drift and performance degradation.
• Experience with model registries, experiment tracking and feature store technologies (for example MLflow, Feast or similar).
• Background in retail or other high-transaction consumer platforms preferred.

Responsibilities

• Architect, build and maintain reliable model training pipelines using reproducible infrastructure‑as‑code patterns.
• Deploy scalable model serving platforms with automated rollout strategies and rollback capabilities.
• Implement monitoring, alerting and automated remediation for model drift, latency regressions and data quality issues.
• Establish model versioning, lineage tracking and experiment reproducibility using model registries and tracking tools.
• Collaborate with data engineering to operationalise feature stores, data validation and lineage workflows.
• Optimise inference cost and latency through model compression, batching and serving architecture enhancements.
• Develop CI/CD workflows for model code, schema migrations and deployment orchestration.
• Create runbooks, governance controls and audit‑ready documentation for ML operations.

Skills

Python

MLOps pipelines

Model monitoring

Data drift detection

Collaboration with stakeholders

Education

Bachelor's or Master's degree in Computer Science, Engineering, Data Science or equivalent practical experience

Tools

Docker

Kubernetes

MLflow

Feast

CI/CD tools

Job description

Forsyth Barnes is seeking a Senior MLOps Engineer to own end-to-end ML pipelines in a high-volume retail tech environment. You will collaborate with data scientists and software engineers to operationalise models from data ingestion to deployment and monitoring, aligning with business KPIs such as conversion and shrink.

The role focuses on scalable infrastructure, model versioning, experiment tracking, and cost-optimised inference, with a strong emphasis on governance, reliability and