> Markdown version of [/jobs/ext/4547470-hiring-ai-engineer-applied-ai-machine-learning-at-palo-alto-ca](https://www.wearedevelopers.com/jobs/ext/4547470-hiring-ai-engineer-applied-ai-machine-learning-at-palo-alto-ca). 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). --- # Hiring: AI Engineer (Applied AI / Machine Learning) at Palo Alto, CA - **Company:** Realtech Services - **Location:** Palo Alto, CA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Feature Engineering, Pytorch, Large Language Models, Information Technology, Machine Learning Operations, Data Pipelines - **Published:** November 3, 2026 - **Apply:** https://www.thejobnetwork.com/job/9f2b00e9-dec9-469c-84cc-ddced67886fc/hiring-ai-engineer-applied-ai-machine-learning-at-palo-alto-ca ## About the Role * Bachelor's or Master's in AI, Computer Science, Math, or related field * Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow) * Experience deploying ML models into production environments Core Competencies: * Machine Learning Development * MLOps & Model Operationalization * Data & Model Quality * Experimentation & Evaluation * Software Engineering * Technical Communication ## Description * We are seeking an AI Engineer to help bring machine learning and generative AI capabilities into real-world products and platforms. You will work at the intersection of data, models, and systems to deliver scalable, production-ready AI solutions., * Select, fine-tune (where needed), and integrate ML and foundation models into production systems * Design and manage the end-to-end ML lifecycle (data, experimentation, deployment, monitoring) * Build robust evaluation frameworks to ensure model quality and performance * Apply relevant techniques in NLP, LLMs, or computer vision depending on domain * Develop data pipelines, feature engineering workflows, and model serving infrastructure * Optimize performance, cost, and scalability across cloud and compute environments * Collaborate with cross-functional teams to deliver AI-powered features