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# AI/ML Platform Engineer

- **Company:** TechniPros, LLC
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Engineering, Continuous Integration, Python (Programming Language), Machine Learning, Standard Sql, Azure Machine Learning, Google Cloud, Delivery Pipeline, Git, Fastapi, AI Platforms, Kubernetes, Machine Learning Operations, Dataiku, Restful APIs, Docker
- **Published:** August 25, 2026
- **Apply:** https://www.dice.com/job-detail/3dc384df-1cb7-42ed-915c-19a4b0ac1d16

## About the Role

· Python

· SQL

· Machine Learning

· MLOps

· LLMOps

· MLflow

· Kubeflow

· Feature Store

· Model Registry

· Docker

· Kubernetes

· FastAPI

· REST APIs

· Git

· Azure / AWS / Google Cloud Platform

· CI/CD

Preferred Skills:

· SageMaker

· Azure ML

· Vertex AI

· Ray

· Airflow

· Dataiku

Mandatory Skills:

· Python

· Machine Learning

· MLOps

· LLMOps

· MLflow

· Kubeflow

· Feature Store

· Docker

· Kubernetes

· Azure/AWS/Google Cloud Platform

· FastAPI

· CI/CD

## Description

We are seeking a Senior AI/ML Platform Engineer to build enterprise-scale Machine Learning platforms supporting model development, deployment, monitoring, and governance. The ideal candidate should have strong expertise in MLOps, LLMOps, cloud-native AI platforms, Feature Stores, and scalable ML infrastructure., · Design enterprise AI/ML platforms and reusable ML pipelines.

· Build scalable MLOps and LLMOps workflows.

· Develop model training and deployment pipelines.

· Implement Feature Stores and Model Registry.

· Build CI/CD pipelines for ML applications.

· Monitor model performance, drift, and data quality.

· Develop REST APIs for model serving.

· Deploy workloads on Azure, AWS, or Google Cloud Platform.

· Implement Responsible AI and model governance.


## Related Videos

- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [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)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [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)
- [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker)

## Related Articles

- [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)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [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)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)