1,174 AI Engineer jobs in United States available for remote work from home. Apply for positions such as Senior Cloud Security Automation & AI Engineer- Remote (Anywhere in the U.S.), Principal Engineer, AI Platform, Staff Software Engineer- AI Platform and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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The engineer will lead the delivery of cloud security automation and AI engagements, including architecture design, implementation, and client handoff. They will also provide technical oversight, mentor junior staff, and support presales activities to drive practice growth.
Define and drive the technical strategy and architecture for the company's AI platform while leading cross-functional efforts to integrate agentic AI systems. You will establish engineering standards for reliability and performance while collaborating with teams to scale AI capabilities across the organization.
You will own the design, development, and operation of foundational AI platform systems to support critical AI products and workflows. You will also drive technical decision-making, mentor other engineers, and collaborate with cross-functional teams to ensure system scalability and reliability.
You will build and evolve production-grade agentic AI systems, managing the full stack from orchestration and pipelines to evaluations and production services. You will also contribute to shared agent frameworks and ensure the reliability, security, and scalability of AI solutions within the Global Commercial Services division.
You will lead the technical design and end-to-end implementation of agentic AI systems, including orchestration, tool use, and RAG pipelines. Additionally, you will own production reliability, establish evaluation standards, and mentor engineers on the AI stack.
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You will build and operate production-grade LLM agent systems, focusing on orchestration, tool-calling, and retrieval pipelines. Additionally, you will develop repeatable evaluation frameworks to measure agent performance and ensure system reliability.
The Applied AI Engineer will design, build, and optimize AI-powered features integrated directly into insurance product workflows. They will collaborate with cross-functional teams to develop LLM-based interactions, automation, and decision-support systems that are reliable and measurable.
The Product Engineer will define end-to-end requirements for AI capabilities and translate model performance into actionable product decisions. They will collaborate with engineering teams to design systems, establish quality metrics, and iterate on AI-powered features.
The intern will build and deploy data pipelines in the cloud using Python, Spark, and SQL within the Databricks platform. They will also assist with ad-hoc data analysis, documentation of data governance protocols, and the creation of data and analytics proofs of concept.
You will design, build, and ship end-to-end AI agents and capabilities, including tools, prompts, and frontend experiences. You will also contribute to shared infrastructure and maintain evaluations to ensure high performance and reliability.
You will design, build, and maintain end-to-end AI agents and capabilities, including tools, prompts, and frontend experiences. You will also contribute to shared infrastructure and collaborate with product and design teams to deliver high-quality legal tech solutions.
You will design, build, and deploy production-ready AI applications, copilots, and agentic workflows for enterprise clients. The role involves integrating AI capabilities into existing business systems while ensuring scalability, security, and operational reliability.
The Automation & AI Engineer will design and implement intelligent automation solutions across IT operations, service management, and observability platforms. They will collaborate with cross-functional teams to build AI-driven workflows, integrate enterprise systems, and drive the modernization of ITSM processes.
You will build and maintain full-stack features for the Gavel AI platform, including agent tools, API endpoints, and front-end components. Additionally, you will write and evaluate AI models, monitor production outcomes, and collaborate with senior engineers on design and code reviews.
Partner directly with clients to design, build, and deploy production-grade AI solutions that address complex business challenges. Lead projects through the full lifecycle from discovery and prototyping to deployment and ongoing optimization.
The role involves designing, developing, and implementing AI solutions by architecting and coding software applications. You will also conduct system testing, debugging, and collaborate with cross-functional teams to drive technical innovation.
The Edge Computing AI Engineer will design, optimize, and deploy machine learning models for resource-constrained edge devices. This role involves performing hardware-aware optimizations and ensuring reliable AI performance outside of traditional data centers.
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Build and maintain internal tools, automations, and AI-driven applications to optimize business workflows across marketing and research. Design and evaluate language model-based workflows while partnering with cross-functional teams to deliver scalable, reliable solutions.
You will own the technical design and delivery of multi-component AI systems, including building evaluation frameworks and data pipelines. You will also collaborate across teams to solve high-priority AI problems and participate in an on-call rotation to ensure system availability.
Build and maintain internal tools, automations, and applications that leverage AI to optimize business workflows. Design and evaluate language model-based solutions for tasks such as classification, summarization, and data extraction.
The role involves building production-quality software that integrates AI capabilities while collaborating with business stakeholders to define requirements. You will also conduct client demonstrations, evaluate AI performance, and contribute reusable technical patterns to the team.
The role involves leading technical requirements conversations with stakeholders and designing end-to-end AI capabilities, including model selection and system integration. You will also be responsible for operationalizing these solutions, monitoring performance, and mentoring other engineers on AI best practices.
Lead the end-to-end lifecycle of AI solutions, from discovery and design to production deployment and monitoring. Collaborate with stakeholders to define success criteria and ensure AI capabilities are effectively integrated into business workflows.
You will design, develop, and maintain scalable backend services and autonomous AI agents to support financial and operational workflows. Additionally, you will lead system design decisions, mentor junior engineers, and ensure the performance and reliability of production-grade AI platforms.
The Edge AI Engineer will design, optimize, and deploy machine learning models for resource-constrained edge devices and embedded systems. This role involves performing hardware-aware optimizations and ensuring reliable AI deployment outside of traditional data centers.
The AI Implementation Engineer will lead the end-to-end technical evaluation, validation, and integration of third-party clinical AI software within imaging environments. This role involves collaborating with clinical leadership to ensure AI solutions align with radiologist workflows while maintaining rigorous technical standards for deployment.
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You will build and iterate on production-ready agentic AI applications by translating complex platform capabilities into intuitive, customer-facing solutions. This role involves partnering with platform engineers to design reusable workflows and integrations that deliver measurable operational value.
You will design and implement backend architecture for agentic AI systems, integrating LLMs with structured insurance data and platform APIs. Additionally, you will collaborate with cross-functional teams to build infrastructure for prompt iteration, evaluation, and production deployment of AI-powered workflows.
Design, develop, and maintain scalable backend services and autonomous AI agents to support financial and operational workflows. Lead system design decisions, mentor junior engineers, and ensure high performance and reliability through rigorous testing and monitoring.
Design and build the decision and orchestration layer for AI agents to determine the optimal execution approach, including model selection, tool usage, and retrieval. Partner with cross-functional teams to ensure AI systems are reliable, secure, and performant in production environments.
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