Top Artificial Intelligence Jobs in France

Lead AlixPartners' AI Transformation practice in France: define AI strategy, identify high-value use cases, prototype and deploy AI solutions (PoCs/MVPs), drive adoption, measure business impact, build methodologies, support business development and mentor colleagues.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Lead end-to-end implementations of QVAC-based AI deployments, act as primary client contact, translate business needs into deployment strategies, coordinate cross-functional teams, define roadmaps and governance, track multiple concurrent projects, and produce executive reporting and documentation for production rollouts.

Design and run AI-enhanced sourcing campaigns, maintain structured talent pipelines in Google Workspace, conduct market research and competitor mapping, automate repetitive tasks, ensure accurate data and excellent candidate experience, and evolve sourcing strategies using free LLM and automation tools.

Lead end-to-end deployments of LLM-powered autonomous agents into enterprise workflows, build custom integrations and data pipelines, collaborate with R&D to convert field feedback into product features, architect production-ready AI solutions for large clients, and serve as a technical consultant for stakeholders.

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.

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.

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.

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.

Lead process excellence engagements using AI and automation: assess industry trends and benchmarks, design AI-integrated future-state processes and AX designs, perform process analysis, build data-driven frameworks and change-impact assessments, create AI agents and visualizations, and contribute to domain knowledge and playbooks.

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.