Top Artificial Intelligence Jobs in France

Lead design and delivery of AI-native customer experience platforms, combining frontend architecture, LLM ecosystems, retrieval/agent orchestration and production delivery. Mentor a small engineering team, prototype and ship AI-enabled products, advise clients, run discovery and workshops, and drive AI-augmented engineering practices and governance.

Hands-on consultant embedded with client teams to define multi-channel acquisition strategies, run performance tests, design measurement frameworks, and apply AI to campaign optimisation, content production, automation, and reporting. Drive implementation, translate data into recommendations, and build scalable, data-driven marketing operations for PE-backed and high-growth businesses.

Own the end-to-end product lifecycle for the ArrowSphere Cloud platform: define and prioritize roadmap, translate user needs into requirements, work with engineering/DevOps/UX to deliver features, integrate AI/GenAI capabilities, manage stakeholders, support go-to-market, and drive adoption through training and enablement.

Lead multiple engineering teams to deliver scalable, reliable AI search and discovery products. Own delivery, execution, and operational excellence; coach engineering managers; shape roadmaps with Product and Design; drive system design, scalability, and performance; and contribute to senior technology leadership and organizational planning.

Own technical customer relationships for Arango's AI product suite: run discovery, design graph and GraphRAG architectures, build prototypes, guide production deployment, define governance/monitoring, and enable customers while feeding product feedback.

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.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

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.

Develop and deploy AI/ML models (anomaly detection, classification, regression, NLP/LLMs) using ESA's structured and unstructured data. Work end-to-end across data engineering, model development, testing, MLOps, and software integration. Communicate results to senior management and present at conferences.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Principal Machine Learning Engineer will lead cross-functional AI initiatives, define ML architecture, establish evaluation frameworks, and influence technical standards across teams.

The Data/AI Product Manager will lead the roadmap for the Data & AI squad, managing product development, strategy, and delivery, focusing on integrating AI into influencer marketing technology.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Apprenticeship supporting design, configuration, testing, and deployment of AI agents using Salesforce Agentforce and Slack AI. Assist with prompt design, troubleshooting, documentation, demos, and Agile team activities while learning from experienced engineers and progressively owning projects.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Design and operationalise large-scale GPU AI/HPC datacentre deployments with partners. Advise customers on power distribution, cooling, MEP integration, run PoCs, troubleshoot scaling and performance, deliver technical workshops, and feed product and infrastructure insights back to engineering and business teams.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.