> Markdown version of [/jobs/ext/3114628-ai-ml-engineers](https://www.wearedevelopers.com/jobs/ext/3114628-ai-ml-engineers). 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 Engineers - **Company:** Synergy Ecp Llc - **Location:** Annapolis Junction, MD, United States - **Experience:** Experienced - **Salary:** $150,000.0 - $270,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, DevOps, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Azure Machine Learning, Data Processing, Real Time Systems, Feature Engineering, Pytorch, Model Validation, Scikit Learn, Data Analytics, Machine Learning Operations, Data Pipelines - **Published:** September 27, 2026 - **Apply:** https://www.juju.com/job/15_348d44d94 ## About the Role * Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn) * Experience with data processing and feature engineering * Understanding of model evaluation and performance tuning * Experience deploying ML in production environments * B.S. degree in a technical field and 3+ years of experience Nice to Have * MLOps / model lifecycle tools * Cloud ML services (AWS SageMaker, Azure ML) * Experience with large-scale or real-time systems ## Description We are looking for AI/ML Engineers to build, deploy, and maintain machine learning models and data-driven systems at scale. What You'll Do * Develop and train machine learning models * Deploy models into production environments * Build data pipelines and workflows for model training and inference * Collaborate with software and DevOps teams for integration * Monitor model performance and optimize over time * Support the full ML lifecycle (data * training * deployment * monitoring) ## Related Videos - [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) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [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) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [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) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)