1,174 AI Engineer jobs in United States available for remote work from home. Apply for positions such as AI Engineer, AI Engineer , AI Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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The AI Engineer will own and ship the internal automation roadmap, building production systems and agentic pipelines. This includes creating tools to reduce manual hours in design and development and integrating various platforms like WordPress and Figma.
Build and improve AI-powered applications and internal tools using LLMs, agents, and retrieval systems. Rapidly prototype and iterate on client projects to turn prototypes into reliable, production-ready systems.
Develop and own end-to-end AI applications for healthcare, including clinical trial matching and billing automation. Optimize inference pipelines and real-time speech recognition systems to reduce clinician burnout.
Design and ship production-grade agentic AI systems and RAG pipelines for federal and commercial clients. Build MCP servers and integrate various LLM APIs to create secure, observable, and scalable AI applications.
The AI Engineer will design and deliver production AI systems across commercial and federal modernization programs. They are responsible for end-to-end development including data, model integration, services, UI, infrastructure, security, and deployment.
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Architect and build full-stack, enterprise-grade platforms using modular agentic workflow patterns. Design and implement intelligent workflows while ensuring system security, scalability, and seamless enterprise integration.
You will spearhead the development of innovative AI-driven solutions and deploy scalable products leveraging LLMs to support the payment orchestration platform. Additionally, you will design and optimize prompts while collaborating with cross-functional teams to integrate AI features into existing payment workflows.
You will build and ship full-stack features while identifying and implementing high-leverage AI automation opportunities. The role involves designing AI agents and integrating LLMs into production workflows to enhance product efficiency.
As an AI Engineer, you will develop and optimize AI components for CoreStory’s narrative intelligence platform. This includes working on LLM integration, vector search systems, and retrieval-augmented generation pipelines.
The AI Engineer will navigate ambiguity and adapt quickly in a fast-moving team while demonstrating ownership by proactively identifying challenges and driving solutions. They will collaborate across distributed teams to design, build, and iterate on innovative AI solutions from concept to production.
The AI Engineer will develop and implement cutting-edge AI solutions to enhance Amplify's product suite and design intricate machine learning models to tackle complex challenges. This role involves collaboration with diverse teams and effective communication of AI concepts.
You will design, develop, and deploy scalable AI software components including foundation models, LLM inference, and agentic workflows. Additionally, you will lead technical AI architecture vision and mentor senior technical leaders across the organization.
You will design, develop, and deploy scalable AI software components including foundation models, agentic workflows, and model orchestration systems. Additionally, you will set the technical direction for enterprise-wide AI architecture and champion responsible AI principles across all system designs.
You will architect and build major components of the AI cloud platform, including GPU and network virtualization stacks and global management planes. You will also lead technical direction across teams, mentor engineers, and ensure the reliability and scalability of the infrastructure.
The engineer will manage the health and security of the SQL Server and Azure cloud data warehouse infrastructure while building internal tools using AI-assisted development. They are responsible for maintaining ETL/ELT pipelines, ensuring data quality, and leading the decommissioning of legacy systems.
The Generative AI Engineer will design, execute, and operationalize fine-tuning workflows for large language models using supervised and reinforcement learning approaches. They will collaborate with cross-functional teams to translate requirements into production-grade solutions while maintaining rigorous evaluation standards.
The Senior AI Engineer will design, implement, and operationalize advanced AI/ML/NLP solutions within AWS cloud-native environments. They will also lead technical efforts, manage CI/CD pipelines, and collaborate with cross-functional teams to optimize model performance for document-centric AI systems.
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The role involves managing AI information security operations, including incident response, playbook development, and securing AI/ML infrastructure. You will collaborate with IT and AI teams to implement security standards, monitor generative AI systems, and ensure compliance with AI governance frameworks.
You will lead the AI Quality engineering team to build evaluation frameworks, observability tools, and diagnostic infrastructure for AI agents. This role involves hands-on coding, managing technical priorities, and partnering with AI Core engineering to ensure reliable and efficient AI system performance.
The Senior Full-Stack AI Engineer will design, build, and ship production-ready software features across the full stack, including frontend, backend, and AI services. They will specifically focus on developing agentic workflows, retrieval-augmented generation (RAG), and integrating large language models into enterprise cybersecurity compliance platforms.
Develop, test, deploy, document, and support secure, maintainable production applications for clinical research and business operations, using AI-assisted and traditional development methods. Collaborate across technical and business teams, maintain regulated-system documentation, and improve reusable practices and standards for AI-assisted development.
You will design and build technical infrastructure for AI governance, including guardrails for LLM gateways and automated risk intake pipelines. The role involves collaborating with cross-functional teams to ensure AI systems meet regulatory, privacy, and safety standards.
Design, develop, deploy, and optimize AI solutions—including generative AI, NLP, and predictive models—for healthcare platforms, integrating them into existing systems and processing large healthcare datasets. Monitor model performance, apply MLOps practices, ensure regulatory and ethical compliance, and identify opportunities to automate workflows and improve operational efficiency.
Embed security throughout the software development lifecycle by conducting threat modeling, architecture and design reviews, code reviews, and vulnerability remediation. Secure AI-enabled and cloud-native systems, and build reusable security patterns and automated controls integrated into CI/CD pipelines.
Design, optimize, and deploy machine learning models for mobile and embedded edge devices, using compression, quantization, and hardware-aware techniques to meet performance, memory, and energy constraints. Build secure inference, update, telemetry, and benchmarking systems; collaborate across hardware and product teams; and maintain technical documentation and responsible AI practices.
The Principal Deployment Engineer will act as a senior trusted advisor to strategic clients, guiding them on maximizing value from nPlan's AI-driven project controls technology. They will also own end-to-end product deployment projects and collaborate with product teams to shape the development of advanced features.
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The Staff Engineer will manage the technical operations, configuration, and observability of workplace AI platforms while ensuring secure integration and access management. Additionally, the role involves translating production technical signals into governance, risk, and compliance narratives for stakeholders.
You will design, develop, and deploy scalable AI software components including foundation models, LLM inference, and multi-agent workflows. Additionally, you will set the technical direction for enterprise-wide AI architecture and mentor engineering teams to ensure responsible and high-performance AI integration.
You will own the quality, evaluation, and continuous improvement of AI agents and prompts to ensure they effectively support small business marketing goals. This involves building technical tooling, defining evaluation rubrics, and diagnosing system failures across models, context, and orchestration layers.
The role involves embedding security into the software development lifecycle for AI-enabled products and cloud-native platforms. You will conduct security architecture reviews, implement automated security controls, and partner with engineering teams to remediate vulnerabilities.
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