Senior MLOps Engineer (Ref: 197482)

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

About Us

Our client operates one of the largest omnichannel retail networks in the United States, supplying apparel, footwear, home goods and beauty products through physical stores and a national e-commerce platform. The organisation focuses on delivering value-driven merchandise and private-label brands to middle-income families, while supporting seasonal campaigns and promotions that create predictable spikes in traffic. Engineering teams support high-volume transaction systems and consumer-facing services that require resilient, scalable infrastructure to maintain fast, reliable shopping experiences.

Job Description

The Senior MLOps Engineer will lead the operationalisation of machine learning at scale across core retail functions including product discovery, pricing, fraud detection and inventory optimisation. The role requires ownership of end-to-end ML pipelines, from data ingestion and feature engineering to model training, deployment and long‑term monitoring. Success will be measured by reduced deployment lead times, stable model performance during promotional peaks and demonstrable improvements in business KPIs such as conversion and shrink. The position collaborates with data scientists, software engineering and business stakeholders to translate business requirements into operable ML systems and governance practices.

Key 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.

Requirements

• 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.
• Practical 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.
• Experience optimising model inference (quantisation, distillation, accelerated runtimes) is a plus.

Benefits

• Competitive salary aligned to market and role responsibilities.
• Comprehensive health, dental and vision insurance options.
• Generous paid time off and paid parental leave policies.
• Professional development budget and access to training resources.
• 401(k) retirement plan with employer contribution options.
• Employee discounts across merchandise and online purchases.
• Flexible hybrid work arrangements and commuter benefits where applicable.

Other

This opportunity will allow a technical leader to shape the MLOps roadmap for a complex retail ecosystem and to influence measurable improvements in customer experience and operational efficiency. Candidates with related expertise in SRE, data engineering or production ML platforms will find rapid impact and career growth potential in this role.