We
are seeking a highly skilled MLOps Engineer to support the IRAS (Item
Recognition as a Service) team, a core component of the ISEE (Intelligent Store
Execution Engine) platform at Sam’s Club.
This role is responsible for owning and evolving the end-to-end MLOps
lifecycle, enabling scalable, production-grade machine learning systems that
power computer vision, RFID integration, and real-time item recognition across
store environments.
You will operate horizontally across multiple ML products and pipelines,
ensuring reliability, scalability, and rapid iteration of ML-driven
capabilities that support frictionless shopping experiences.
Key Responsibilities
MLOps Lifecycle Ownership
Own the full ML lifecycle: data ingestion, model training, validation, deployment,
monitoring, and retraining
Build and maintain robust model pipelines for computer vision and IRAS-based
item recognition systems
Implement data and model monitoring (drift detection, performance degradation,
alerting)
Refactor and productionize data science code into scalable, reusable services
Platform & Pipeline Engineering
Design and operate end-to-end ML pipelines (data → training → evaluation →
deployment → feedback loops)
Enable model versioning, reproducibility, and governance at scale
Support real-time and batch inference systems tied to RFID + camera fusion
pipelines [Sam\'s iSEE POC | PowerPoint]
CI/CD & DevOps for ML
Build and maintain CI/CD pipelines for ML workflows (model builds, testing,
deployment)
Automate testing, validation, and release processes for ML models and data
pipelines
Ensure high availability, rollback capability, and release governance
Cross-Product / Horizontal Support
Serve as a shared MLOps capability across multiple IRAS and ISEE initiatives
Partner with Data Scientists, CV/ML Engineers, and Platform teams to accelerate
model delivery and adoption
Standardize tools, frameworks, and best practices across teams
Proven experience owning MLOps lifecycle end-to-end in production environments
Strong Python engineering experience (building scalable services, pipelines)
Hands-on expertise with CI/CD pipelines for ML systems
Experience supporting multiple ML products or platforms simultaneously
MLOps & ML Tooling
Deep knowledge of:
MLflow / Kubeflow (or similar orchestration frameworks)
Model versioning, experiment tracking, and pipeline orchestration
Data monitoring, drift detection, and model observability
Strong understanding of machine learning fundamentals and lifecycle management
Data & Cloud Ecosystem
Experience with:
GCP (preferred), Azure, or hybrid environments
Big Data ecosystems (Databricks, Spark, distributed processing)
SQL and large-scale data pipeline development
Familiarity with cloud-based ML infrastructure and scaling strategies
Engineering & Systems
Proficiency in:
Shell scripting and automation
Containerization and orchestration (Docker, Kubernetes)
Building reliable, scalable backend systems for ML inference
Preferred / Nice-to-Have
Experience with computer vision pipelines (CV/ML models, object detection, item
recognition)
Exposure to RFID data integration or sensor fusion systems
Experience working in retail, edge + cloud hybrid environments, or real-time
inference systems
Familiarity with GPU-based inference optimization and performance tuning
What Success Looks Like
Fully automated, production-grade ML pipelines supporting IRAS item recognition
Reduced latency and improved reliability of model deployment and inference
workflows
Standardized MLOps practices across ISEE teams
High-confidence, monitored models with continuous improvement loops
Top
Skills Details
Top
Skills' Details
1. Experience owning MLOps lifecycle, from data monitoring to refactoring data
science code to building robust ML model lifecycle.
2. Python Engineering
3. CI/CD
4. Experience with MLOps driven data science outcomes and handle ML Engineering
horizontal helping multiple products and initiatives.
5. have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow,
Python/R, SQL, Big Data, GCP, Shell scripting.
Worksite
Address
1701
West State Highway 114,Grapevine,Texas,United States,76051
Workplace
Type
Hybrid
EVP
You
are getting to work with several teams that are on the cutting edge of
technology for retail.
Work
Environment
Fast
paced, POC so like a startup, must be a self starter
Additional
Skills & Qualifications
Must
be W2. Prefer to have them in DFW area to go to the Grapevine Sam\'s Club 1-2
times per week but not an absolute must.
Business
Challenge
By
executing this IRAS project, we will be able to take on more of their monarch
work going into next year. It will allow their item recognition service to
exceed 85-90% accuracy
Information
Locations Position Open to Only localsIndustry Information TechnologyStatus OpenJob Age 30 Day'sCreated Date 07/07/2026No.of Positions 1Duration 6+ monthsZip Code
