Top Artificial Intelligence Jobs in Paris

Lead the AI platform that powers Photoroom's products: build an AI gateway, no-code tooling, and agentic multi-step visual workflows. Partner with engineers and researchers to turn model capabilities into reusable primitives, prioritize platform work across teams, and use data to define, measure, and iterate on shipped features.

Join the Incidents Squad to build, scale, and stabilize LLM-powered features across the incident lifecycle. Implement agentic workflows, improve reliability, latency, robustness, observability, and productionize AI systems while partnering with ML engineers and shaping platform orchestration and performance.

Design and deploy production AI agents and specs-to-production pipelines to automate engineering tasks (bug triage, diagnostics, patch suggestions, code review). Build automated workflows from design to merge-ready code, improve test generation and AI-assisted validation, consolidate team AI practices, and co-own the AI toolchain roadmap while embedding with feature teams.

Design, build, and scale AI-powered internal platforms, assistants, and automation tools. Integrate LLMs, agents, RAG and orchestration into workflows, define governance and cost controls, and drive AI adoption across engineering, data, and product teams.

Lead the Business Insights & AI vertical to turn complex studio data into trusted insights and AI-driven operations. Set strategy, run discovery, deliver AI/ML features (summaries, recommendations, agents), measure outcomes, and partner cross-functionally to drive adoption, data quality, and business impact for boutique fitness studios.

Lead the design and delivery of a governed VaaS data and AI platform: define governance, ontology, and APIs; build RAG/LLM pipelines, vector storage, and agentic integrations; coordinate analysts and engineers; translate business needs into high-impact AI use cases and automate workflows across ERP/CRM and operational systems.

Translate TecAlliance business problems into production-ready AI solutions: define use cases, prototype with ML/LLM components, iterate with stakeholders, and hand over to production while documenting and scaling best practices.

Design, build, and deploy Generative AI solutions: benchmark models, prototype applications, implement prompting, RAG, vector search, fine-tuning, ensure security/compliance, monitor production performance, and document best practices while collaborating with cross-functional teams.

Lead development of a proactive Sales AI assistant: define strategy and roadmap, work with designers, engineers and sales to build prompts and connectors, automate prospecting and offer generation, prioritize high-impact automation, and drive metrics like time saved and win rate.

Lead the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

The Playable AI Engineer will develop and scale interactive ads using AI and playable development skills, collaborating with various teams to optimize ad performance.

Lead architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines (node-based or code-first), train LoRAs end-to-end, integrate control techniques (ControlNet, IP-Adapter, inpainting), deploy internal GPU-backed tools, automate QC against product references, and partner with design to own the technical roadmap and enable non-technical teams.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Lead design and implementation of CI/CD and AI-related product features, owning end-to-end delivery. Build and secure CI/CD systems, work with containers, registries, and Kubernetes, extend agentic AI into customer experiences, guide architecture, and collaborate closely with Product and front-end teams.

Build and own end-to-end features for Chainguard's AI-enabled CI/CD and supply-chain security products. Design, implement, and iterate on integrations like hardened GitHub Actions, container tooling, and agentic AI features. Drive architectural decisions, collaborate with Product and front-end teams, and ship secure, production-ready developer tools.

Design and implement AI components for in-game event recognition and model optimization, focusing on multimodal learning and performance tuning.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.