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# ML Infrastructure & MLOps Engineer

- **Company:** AllSTEM Connections
- **Location:** Ontario, CA, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Airflow, Program Optimization, Continuous Delivery, Continuous Integration, Information Engineering, Distributed Systems, Azure Machine Learning, Cloud Platform System, Delivery Pipeline, Reliability of Systems, Backend, AI Platforms, Kubernetes, Deployment Automation, Machine Learning Operations
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/5d969548-f82c-4093-b8ab-962255785517

## About the Role

Experience:
o5 to 10+ years of hands-on experience designing and operating large-scale distributed ML platforms.
oProven track record of supporting production-grade ML workflows in cloud environments.
Technical Mastery:
oDeep expertise in container orchestration, specifically GKE (Google Kubernetes Engine) or equivalent enterprise Kubernetes environments.
oHands-on experience building scalable ML pipelines (e.g., Kubeflow, Airflow, TFX).
oStrong proficiency in distributed training strategies, feature store management, and model serving infrastructure.
Soft Skills & Attributes:
oPragmatic Mindset: Strong ownership-driven work style focused on consistency, system reliability, and cost-awareness.
oEffective Communicator: Ability to collaborate seamlessly with highly technical researchers and platform engineers alike.

Preferred Qualifications
Prior experience working within dedicated, tier-1 enterprise ML/AI platform teams.
Deep knowledge of distributed systems backend optimization and infrastructure-as-code (IaC).

## Description

ML Infrastructure & Container Orchestration
Distributed Clusters: Architect and maintain high-performance training and serving infrastructure utilizing Google Kubernetes Engine (GKE).
Model Optimization: Design and implement high-efficiency optimization pipelines, including advanced knowledge distillation and foundational training tooling.
Platform Scaling: Build, monitor, and optimize shared ML systems to ensure maximum infrastructure uptime, pipeline reliability, and cloud cost-efficiency.

Data Engineering & Pipeline Automation
Workflow Automation: Build robust, automated pipelines for standardized model training, validation, and continuous deployment (CI/CD for ML).
Feature Platforms: Develop scalable data sampling and feature-generation platforms to accelerate research experimentation cycles.
Onboarding & Usability: Drive high platform adoption by building intuitive, standardized deployment tools that decrease onboarding speed for research and engineering teams.

Collaboration & Governance
Cross-Functional Bridge: Collaborate closely with ML researchers and core software engineers to translate theoretical models into highly scalable production systems.
Methodical Execution: Apply a disciplined, data-backed approach to identify infrastructure bottlenecks, reduce time-to-market, and stabilize complex deployments.


## Related Videos

- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)