> Markdown version of [/jobs/ext/2622881-ai-ml-platform-engineer](https://www.wearedevelopers.com/jobs/ext/2622881-ai-ml-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Platform Engineer - **Company:** TechniPros, LLC - **Location:** Fairfax, VA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Engineering, Continuous Integration, DevOps, 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 28, 2026 - **Apply:** https://www.dice.com/job-detail/2174a294-41e8-4664-9e66-aa77bdb3cef4 ## 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 ## 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. * Collaborate with Data Science and DevOps teams. ## Related Videos - [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) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [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) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)