Manager Mlops Engineer Jobs (NOW HIRING)

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Client: Walmart Inc.
Location: Grapevine, TX (Dallas, TX) - hybrid onsite
Title: MLOps Engineer
Duration: 6 months+, possible extension
Rate: $80/hr+, depending on experience
Description
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 system
Implement data and model monitoring (drift detection, performance degradation, alerting)
Refactor and productionize data science code into scalable, reusable service
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 pipeline
Ensure high availability, rollback capability, and release governance
Cross-Product / Horizontal Support
Serve as a shared MLOps capability across multiple IRAS and ISEE initiative
Partner with Data Scientists, CV/ML Engineers, and Platform teams to accelerate model delivery and adoption
Standardize tools, frameworks, and best practices across team
Required Skills & Experience
Core Requirements (Must Have)
Proven experience owning MLOps lifecycle end-to-end in production environment
Strong Python engineering experience (building scalable services, pipelines)
Hands-on expertise with CI/CD pipelines for ML system
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 environment
Big Data ecosystems (Databricks, Spark, distributed processing)
SQL and large-scale data pipeline development
Familiarity with cloud-based ML infrastructure and scaling strategie
Engineering & System
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 system
Experience working in retail, edge + cloud hybrid environments, or real-time inference system
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 workflow
Standardized MLOps practices across ISEE team
High-confidence, monitored models with continuous improvement loop
Top Skills Details
Top Skills' Detail
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