AI Engineer
You will apply generative AI to threat research and fraud detection, including building a Gen AI stack in the cloud. You will also collaborate with other tech teams to develop and integrate AI-powered features into DataDome products.
42 AI Engineer jobs in France available for remote work from home. Apply for positions such as AI Engineer, Staff AI Engineer - Architecture skills, Senior Applied AI Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will apply generative AI to threat research and fraud detection, including building a Gen AI stack in the cloud. You will also collaborate with other tech teams to develop and integrate AI-powered features into DataDome products.
The Staff AI Engineer will lead the design and implementation of production-ready agentic workflows and AI orchestration layers. They will also mentor engineering squads, establish LLMOps standards, and bridge the gap between AI research and scalable production microservices.
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 build and operate production infrastructure supporting AI-related workloads while improving reliability through SLOs, observability, and capacity management. Additionally, you will participate in incident response and collaborate with engineering teams to automate deployment workflows.
You will build production AI features end-to-end, taking them from initial concept through to deployment. This involves working across the full stack, including API design, data integration, and creating streaming interfaces for document-heavy UIs.
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You will deliver end-to-end functionality across a TypeScript/React front end and a Golang backend while building products for partner integrations. Additionally, you will help shape the architecture of the platform on AWS and maintain high standards for code review and verification.
You will be responsible for turning LLM intelligence into reliable production systems by managing context engineering, memory, and agent orchestration. You will also own the evaluation and guardrails system to ensure the safety and quality of the AI product.
You will lead the architecture and development of production-grade AI agents and intelligence products to optimize payment processes. This involves setting technical direction for agentic systems, ensuring reliability, and mentoring engineers to deliver complex cross-team AI projects.
You will design and implement AI-driven solutions, including RAG techniques and agentic systems, to enhance clinical research and drug development. You will collaborate with cross-functional teams to integrate these AI features into a scalable, production-grade platform.
You will own features end-to-end, from design through implementation and rollout, while collaborating with product and engineering teams. Additionally, you will extend agentic AI foundations into customer-facing product experiences across the company's suite.
Design and build execution primitives for AI workflows within the Kestra engine and maintain plugins for LLM providers. Ensure AI executions are observable, debuggable, and translated into reusable blueprints.
Design and industrialize generative AI solutions, including LLM architectures, RAG, and autonomous agents to transform business processes. Manage the full AI lifecycle from requirement analysis and prototyping to production deployment and performance monitoring.
The GTM Engineer will architect and scale AI-powered growth systems across sales, marketing, and product functions. They will lead the development of AI agents and workflows to automate the customer lifecycle and provide actionable growth signals.
Develop cutting-edge GenAI solutions and own production rollouts of GenAI applications. Serve as a trusted technical advisor to customers and collaborate cross-functionally with product and engineering teams.
Develop cutting-edge GenAI solutions and own production rollouts of GenAI applications. Serve as a trusted technical advisor to customers and collaborate with product and engineering teams.
Develop cutting-edge GenAI solutions and own production rollouts of GenAI applications. Serve as a trusted technical advisor to customers and collaborate with cross-functional teams.
You will design and implement autonomous AI agents to streamline business processes across various departments. This involves translating business needs into technical requirements, developing necessary tools and APIs, and monitoring agent performance.
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You will own the end-to-end technical execution of infrastructure design, build, and production rollout for strategic customers and ISV partners. Additionally, you will translate repeated infrastructure challenges into reusable platform capabilities to improve the core Nebius AI cloud platform.
You will design, build, and maintain scalable AWS infrastructure and Kubernetes clusters to support machine learning workflows. Additionally, you will develop internal developer tools and enhance platform observability, security, and CI/CD pipelines.
You will design and deploy machine learning systems for document understanding, classification, and recommendation to enhance product automation. You will own the full lifecycle of these solutions, from problem framing and data strategy to production monitoring and continuous improvement.
The AI Benchmark Engineer will design, build, and validate rigorous multilingual software tasks to evaluate large language models. Responsibilities include creating realistic task environments, writing deterministic verifier scripts, and performing quality assurance through a multi-layer review process.
You will own and operate the infrastructure powering sovereign AI, including Linux environments and cloud systems. You will also collaborate with research and product teams to troubleshoot incidents and ensure high availability for AI workloads.
Design and deploy AI products such as copilots and conversational agents by integrating LLMs and RAG architectures into complete applications. Conduct AI benchmarks, develop POCs/MVPs, and provide strategic AI roadmaps for clients.
The role involves designing and deploying high-impact AI Agent solutions on the Moveworks ServiceNow platform to solve complex customer business challenges. You will act as a technical bridge between customers, product management, and engineering to drive product adoption and evolution.
The role involves designing and deploying high-impact AI Agent solutions on the Moveworks ServiceNow platform to solve complex customer business challenges. It requires full-stack ownership of the delivery lifecycle and collaborating with product teams to influence the platform's evolution based on customer feedback.
The role involves spending roughly half the time in the field assisting new customers with POCs and technical onboarding, and the other half building prototypes, exploring emerging AI techniques, and translating field insights into product direction. Responsibilities include building demos across the portfolio, supporting customer validation, and feeding grounded feedback into the product roadmap.
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You will build and scale an AI platform that enables internal teams to autonomously create, monitor, and iterate on AI agents. This involves designing system architecture, integrating AI agents into internal business tools, and ensuring the safety and accuracy of automated operational tasks.
You will design and implement state-of-the-art AI systems for a medical conversational assistant, focusing on architecture, reliability, and user experience. Additionally, you will integrate these AI capabilities with existing healthcare services and mentor other engineers to foster a strong product culture.
Design and implement full-stack features for the Data Intelligence Platform while leveraging AI tools to accelerate development and improve code quality. Collaborate with product managers and team members to ensure stable, scalable, and maintainable production-grade solutions.
You will design, implement, and scale the platform's core backend systems and data pipelines while leading end-to-end development for critical features. Additionally, you will define architectural standards for security and identity management while collaborating with cross-functional teams to shape the platform's future.
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