> Markdown version of [/jobs/ext/3586775-artificial-intelligence-engineer](https://www.wearedevelopers.com/jobs/ext/3586775-artificial-intelligence-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). --- # Artificial Intelligence Engineer - **Company:** A P T Enterprises LLC - **Location:** United States (Remote available) - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Engineering, Continuous Integration, Python (Programming Language), Machine Learning, Natural Language Processing, NoSQL, Recommender Systems, Tensorflow, Pytorch, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Generative AI, Build Management, Scikit Learn, Kubernetes, Apache Kafka, Free and Open-Source Software, Machine Learning Operations, Terraform, Docker, Databricks - **Published:** October 5, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/lyu0p3ucmw ## About the Role Python (PyTorch, TensorFlow, Scikit-Learn) LLMs, GenAI, RAG pipelines Spark, Kafka, Databricks SQL / NoSQL databases MLflow, Airflow, Kubeflow Docker, Kubernetes, Terraform AWS, Azure or GCP What we are looking for Strong hands-on AI / ML engineering experience Experience deploying production-grade AI systems Cloud-native mindset Strong problem-solving and communication skills Nice to have: Edge AI, IoT, robotics, NLP/CV specialization, open-source contributions. ## Description You will work on NLP, computer vision, recommender systems, predictive analytics and generative AI platforms, collaborating with data scientists, software engineers and product teams. What you will do Design, build and deploy machine learning and deep learning models Develop NLP, computer vision, recommender and predictive analytics pipelines Build and integrate generative AI and LLM-based services Deploy AI models on AWS, Azure or GCP in scalable production environments Build CI/CD and MLOps pipelines for continuous training, deployment and monitoring Optimize models for real-time, batch and edge processing Monitor production models and continuously improve performance Ensure ethical, explainable and responsible AI practices