Design, build, and deploy AI agents to automate enterprise workflows and business processes. Integrate AI capabilities with platforms like Microsoft Dynamics 365, SAP, and ServiceNow to improve operational efficiency.
Remote AI Engineer Jobs (2,147)
Search = AI Engineer
Design and build production-ready AI automations integrating Python, REST APIs, and LLMs to drive cost savings across global operations. Develop full-stack tooling and agentic workflows while maintaining shared component libraries and documentation.
Design and implement end-to-end agentic AI systems and multi-domain agent orchestration using Google Vertex AI and Gemini models. Develop proprietary communication protocols for LLM-to-frontend streaming and manage dual deployment strategies across serverless and managed runtimes.
Generative AI Platform Engineer
Accenture Federal Services
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Full Time
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2 Weeks Ago
Accenture Federal Services
Operationalize and harden LLM/RAG services for federal programs, focusing on reliability, safety, and low hallucination rates. Implement AI governance, observability, and performance engineering to optimize latency and cost.
Forward Deployed Engineer - Agentic AI for Clinical Development
Medable
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Full Time
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2 Weeks Ago
Medable
Lead technical engagements with customers to deploy agentic AI systems for clinical development. Design and implement tailored solutions that integrate with existing eClinical systems and ensure compliance with regulatory standards.
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Senior Software Engineer II- AI Developer Experience (Remote USA)
Optro
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Full Time
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2 Weeks Ago
Optro
Architect and build the Shared AI Tooling Layer (SATL) to integrate LLMs into the engineering environment. Lead technical scouting of emerging AI frameworks and build safe execution environments for programmatic codebase modifications.
Build and scale backend systems and AI-driven workflows to power core product functionality. Design low-latency search, inference, and orchestration layers while managing RAG retrieval and platform stability.
Design and build production-grade AI employees and agentic workflows using a proprietary platform and external integrations. Act as the technical owner for client delivery, translating requirements into scalable software foundations and conversational systems.
Act as the primary liaison between the End User Computing team and the AI enablement group to deploy and optimize AI tools across Mac and Windows endpoints. Responsibilities include packaging applications, configuring endpoint policies, and monitoring performance impacts of AI software.
Build, train, and deploy machine learning models to detect security threats and manage exposure. Develop production AI features including prompt orchestration, RAG, and automated performance pipelines.
Drive the transition to an AI-augmented QA model by validating AI-generated test results and performing targeted exploratory testing. Develop and maintain scalable automation suites using Playwright to ensure high code quality for Salesforce-based legal software.
iOS Engineer II (AI Native), Family AI Lab – San Francisco Bay Area
Life360
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Full Time
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2 Weeks Ago
Life360
Rapidly transform rough ideas into clickable iOS prototypes and native surfaces like Live Activities and Dynamic Island. Collaborate directly with the founder and design team to iterate on an AI-powered family operating system using aggressive AI tooling.
Design and implement AI-powered features and natural language interfaces for a digital banking platform. Integrate LLMs and AI APIs while ensuring security, compliance, and reliability within a fintech environment.
The engineer will design and implement agentic features from prototype to production, including building and maintaining agent loops and tool layers. They will also work directly with client stakeholders to refine requirements and ensure alignment on expected outcomes.
Design and maintain the foundational infrastructure and internal provisioning platform for generative AI applications. Ensure AI integrations are secure, performant, and cost-effective while implementing observability and governance systems.
Design and maintain the foundational infrastructure and internal provisioning platform for generative AI applications. Ensure AI integrations are secure, performant, and cost-effective while implementing observability and governance systems.
Internal AI Product Engineer (f/m/x) - remote 19 APPLICANTS
refurbed
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Full Time
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2 Weeks Ago
refurbed
Lead the design and delivery of AI-powered internal tools and backend services to transform the company into an AI-native organization. Partner with leadership to define the AI product roadmap and drive cross-functional adoption of automation tools.
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Evolve and maintain the cloud-native AI ecosystem including Kubeflow, Feast, and Spark-on-Kubernetes to support scalable AI deployment. Design self-service internal tools and APIs to empower distributed teams to manage their own ML lifecycles.
Develop novel computer vision models to diagnose health conditions in pets through video analysis. Build and maintain the full-stack infrastructure, including backend services and APIs, to make these models accessible to veterinarians.
Own the technical vision and architecture for the AI platform, focusing on Agent, Skill, and Tool frameworks. Scale retrieval systems and knowledge graphs to turn industrial data into customer-specific intelligence while improving developer experience.
Lead the security, governance, and risk management of AI systems, ranging from enterprise tools to agentic AI pipelines. Architect a multi-layer security framework and conduct adversarial red-teaming to prevent threats like prompt injection and data poisoning.
Design and deploy scalable AI-driven applications using Large Language Models, NLP, and speech-based interfaces. Develop backend services and cloud-native AI workloads to support intelligent automation and enterprise solutions.
Design and deploy scalable AI-driven applications using Large Language Models, NLP, and speech-based interfaces. Develop backend services and cloud-native AI workloads to support intelligent automation and enterprise solutions.
Design and deploy scalable AI-driven applications using Large Language Models, NLP, and speech-based interfaces. Develop backend services and cloud-native AI workloads to support intelligent automation and enterprise solutions.
Design and deploy scalable AI-driven applications using Large Language Models, NLP, and speech-based interfaces. Develop backend services and cloud-native AI workloads to support intelligent automation and enterprise solutions.
Enterprise AI Solutions Engineer 24 APPLICANTS
Maynard Nexsen
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Full Time
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2 Weeks Ago
Maynard Nexsen
Design, build, and deploy custom AI agents and automated workflows using the firm's approved platform stack. Translate business requirements from legal and administrative leadership into governed, production-scale AI solutions.
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Lead end-to-end technical implementations and custom integrations of AI tools for enterprise customers. Provide pre-sales technical support and collaborate with product teams to refine the AI roadmap based on customer feedback.
Iterate on AI-native growth infrastructure and identify automation opportunities across the customer journey to reduce human touchpoints. Focus on reducing CAC and increasing self-serve conversion through the deployment of AI tools and workflows.
The role involves utilizing AI-augmented tools and agents to accelerate the software development lifecycle and automate repetitive engineering tasks. The engineer will build scalable Cloud SaaS applications and partner with Product Managers for rapid prototyping and requirements discovery.
Integrate AI tools into existing .NET and C++ applications on Windows systems. Develop interfaces for model interaction and manage the deployment and maintenance of AI components.
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