Top Artificial Intelligence Jobs in France

Lead AQEMIA’s ML research organization of approximately 15 scientists and researchers. Own the predictive modeling roadmap, guide frontier architectures, scale production-ready high-throughput workflows, and ensure scientific rigor, robustness, and measurable platform impact. Collaborate with chemistry, biology, physics, engineering, and leadership teams to advance AI-powered drug discovery.

Design, build, and deploy production-grade autonomous agents, multi-agent systems, and LLM-powered enterprise applications. Develop reusable orchestration patterns, integrate models with APIs, databases, tools, and enterprise systems, and implement structured outputs, memory, monitoring, tracing, evaluation, and recovery workflows. Deploy secure, scalable AI solutions primarily on AWS while applying MLOps and AIOps practices. Collaborate with engineering, data, cloud, and business teams.

Build and secure cloud infrastructure for production AI solutions across AWS and Azure. Responsibilities include designing CI/CD pipelines, implementing Infrastructure as Code, integrating security scanning and compliance controls, managing Docker and Kubernetes environments, configuring IAM and network security, monitoring and logging systems, improving reliability, and supporting penetration testing remediation.

Build, deploy, and operate production AI agents and multi-agent systems. Own end-to-end delivery including agent development, CI/CD, infrastructure, monitoring, evaluations, regression testing, context engineering, and production debugging. Partner with CX, RevOps, and Data teams to identify needs, ship solutions rapidly, and shape the agent platform architecture. The role requires a pragmatic whole-system thinker with production engineering, DevOps, LLM, prompt engineering, and evaluation experience.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Build and operate AI agents, assistants, knowledge-base pipelines, APIs, integrations, and user-facing tools for company-wide AI adoption. Own features from design through production, including testing, CI/CD, observability, security guardrails, responsible AI, metrics, documentation, incident response, and continuous improvement based on internal user feedback.

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

Design, build, and operate production-grade AI agents, assistants, automations, shared frameworks, knowledge bases, and system integrations. Partner with business stakeholders to identify automation opportunities and deliver measurable solutions. Ensure security, privacy, reliability, observability, cost efficiency, and responsible AI practices. Contribute to AI-assisted engineering standards, documentation, incident response, hiring, and mentoring across the organization.

Develop and evaluate classical, quantum, and hybrid machine-learning methods for quantum computing. Responsibilities include designing models, improving quantum-system calibration and control, benchmarking methods, contributing to the MerLin framework, publishing research, presenting findings, collaborating with academic and multidisciplinary teams, and mentoring junior researchers.

Manage relationships with AI-native startups, scaleups, and labs in France. Understand customer needs, develop strategic opportunities, promote NVIDIA AI platforms, coordinate technical and business resources, build customer references, identify emerging use cases, and represent NVIDIA at industry events. The role requires deep AI industry knowledge, startup ecosystem connections, strategic planning, communication skills, and familiarity with NVIDIA’s software, server, and cloud stack.

Lead go-to-market strategy and commercialization for digital assets in EMEA. Drive product positioning, client engagements, regulatory and market analysis, cross-functional alignment, enablement materials, and influence global product roadmaps to scale adoption.

Design, train, and deploy state-of-the-art multimodal acoustic models for underwater detection, classification and localization. Build end-to-end training pipelines with self-supervised pre-training and fine-tuning, fuse multiple sensor modalities, optimize and distill models for edge deployment on constrained hardware, shape data collection strategy from fleet sensors, and iterate using sea trials.

Six-month legal internship supporting Qonto’s fintech products and AI initiatives. Responsibilities include drafting and negotiating product contracts, analyzing banking and payments regulations, applying the EU AI Act, addressing data protection requirements, and collaborating with Regulatory, Product, Strategy, and Growth teams. The role offers hands-on experience in commercial and product law within a European financial technology company.

The Senior AI Product Manager will own the Data & AI roadmap, defining strategy and leading product initiatives from discovery to deployment while optimizing processes and documentation.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

PhD-level research intern to investigate and prototype 3D computer vision methods (SLAM, VIO, localisation, reconstruction). Survey SOTA, design and benchmark algorithms under real-world conditions, convert ideas into prototypes, and report results to a distributed European team focused on deployable defence-grade spatial perception.

Lead enterprise AI adoption by helping business functions rethink their roles, driving operational use of AI, and accelerating adoption across VPTech. Responsibilities include designing trainings, hackathons, and challenge sessions; challenging existing processes with business owners and BPOs; and supporting executives with long-term AI adoption strategies.

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 the development of AI-assisted engineering practices and tooling, enhance developer experience, and implement security measures across the engineering team.

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.

Lead and co-direct an AI research program to develop post-training RL methods that align large biological models to high-throughput experimental measurements. Design and run model training, debugging, and evaluation; architect training infrastructure; collaborate closely with experimental teams; publish methods and datasets; potentially grow into a leadership/co-founder role.

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.

The role involves delivering technical solutions, integrating AI/ML models, developing software, and mentoring junior engineers while ensuring customer satisfaction through engagement and solution design.