Top Artificial Intelligence Jobs in France

Lead the architecture and production delivery of AI agents and intelligence systems for payments. Build reliable, low-latency LLM applications with tool use, orchestration, memory, RAG, multilingual conversations, evaluation, observability, and safety guardrails. Develop merchant intelligence and AI operations automation, mentor engineers, establish technical direction, and collaborate across Product, Data Science, and engineering teams in a compliance-sensitive global environment.

Design and deploy optimized on-device AI models for gaming, biosensing, and peripheral applications. Develop real-time biosignal and sensor-processing systems, optimize models through quantization and pruning, write production-quality C++ inference runtimes, create APIs, and profile performance across CPU, GPU, and NPU targets.

Build and maintain production-grade agentic AI systems for an internal AI platform. Design AWS infrastructure, knowledge graph solutions using Neptune or Neo4j, and LLM-based reasoning and retrieval workflows. Troubleshoot integrated systems, collaborate across teams, influence technical direction, and support engineering best practices. The contract is expected to last 4–6 months, with possible extension, and requires strong Python, AWS platform engineering, and agentic AI expertise.

Advise clients on creating and capturing AI and data value. Define enterprise AI strategies, assess organizational maturity, design technology and operating-model blueprints, build business cases and roadmaps, embed Responsible AI, develop ecosystem plans, and lead large-scale technology transformations. Engage senior stakeholders, lead delivery workstreams, identify capabilities needed to operationalize AI solutions, and create AI-first products and commercialization opportunities.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Manage and prosecute a global patent portfolio in wireless and video technologies; prepare and file EP/US/PCT and related applications; collaborate with inventors, licensing and litigation teams; map claims to standards and products; support post-grant reviews, assertion efforts, acquisitions, and SSO declarations.

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.

Build and deploy production-grade LLM capabilities for the Kayzen Console. Responsibilities include developing AI-backed APIs and full-stack features, implementing RAG, embeddings, agents, and tool calling, and establishing evaluation, monitoring, observability, and feedback systems. The role optimizes quality, latency, reliability, and cost; troubleshoots production issues; collaborates with Product, ML, and Engineering; and turns prototypes into maintainable AI products.

Develop and evaluate AI and machine learning systems with emphasis on fairness, transparency, explainability, robustness, soundness, privacy, and bias mitigation. Participate across the AI lifecycle, from data preparation and coding through deployment and support. Advise clients, support proposals, document decisions, create responsible AI guidelines and training, and stay current with AI ethics and technology developments.

Owns 2–4 AI automation and agentic technology projects concurrently, from business-process discovery and prioritization through hands-on development or collaboration with builders. Partners with cross-functional teams and VP-level stakeholders, measures outcomes such as time saved, cost avoided, and adoption, and iterates on shipped solutions. The role combines project ownership and stakeholder alignment with hands-on building using low-code automation, code integrations, agents, and CI pipelines.

Leads global privacy and AI legal matters for a SaaS employment platform. Advises Product, Engineering, Security, and business teams on privacy by design, AI governance, data protection, workforce data, incidents, regulatory compliance, vendor reviews, transfers, and contract terms. Builds scalable policies, controls, templates, training, and customer-facing trust materials while monitoring global privacy and AI regulations.

Build and deploy production-ready Generative AI and LLM solutions across Aramis Group, including conversational agents, voice applications, RAG architectures, and integrations with APIs and vector databases. Develop AI pipelines using Python, SQL, Docker, and cloud technologies; monitor and optimize production systems; investigate incidents; evaluate technical solutions; and share findings with an international, multidisciplinary team.

In this role, you'll identify AI opportunities for enterprise clients, design scalable AI solutions, prototype implementations, and drive customer value from AI deployments while providing industry expertise.

Leads AI- and data-driven manufacturing transformation strategies for Life Sciences clients. Advises C-suite and operations leaders, develops AI strategies, business cases, roadmaps, operating models, and responsible AI plans, and oversees large-scale greenfield and brownfield manufacturing programs. Drives digital twin, automation, data platform, advanced analytics, generative AI, and AI/ML deployments from proof of concept through enterprise scale. Builds executive relationships, originates opportunities, manages partner ecosystems, develops manufacturing capabilities, and leads teams, delivery, financial performance, and risk.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Own design, deployment, and operation of high-performance AI GPU fabrics and datacenter networking (RoCE, RDMA, BGP/ECMP, EVPN/VXLAN). Provide staff-level architecture, cross-team technical leadership, incident escalation, automation (Python/Bash), and operational standards for global datacenter and edge deployments, including inter-datacenter transport and security.

Design, build, and operate the AI storage layer for GPU-heavy edge and core deployments. Architect and optimize storage for distributed training and inference, integrate storage with Kubernetes (CSI), tune performance (RDMA, NVMe-oF, GPU Direct Storage), lead reliability and operational practices, and provide staff-level architectural ownership and cross-team influence while executing hands-on storage engineering.

Design, train, evaluate, and deploy production computer vision and vision-language systems for industrial applications. Build pipelines for detection, segmentation, OCR, tracking, multimodal reasoning, and real-time edge or cloud inference. Develop data and evaluation workflows, fine-tune foundation models, and optimize deployments through quantization, pruning, and hardware acceleration. Collaborate across AI, engineering, research, and product teams to deliver scalable, robust visual AI solutions.

Design and operate production backend services and AI infrastructure, including model-serving pipelines, inference services, data pipelines, RAG systems, vector stores, APIs, and microservices. Build scalable real-time and batch systems, integrate LLM, computer vision, and machine learning models, and manage deployment, observability, reliability, and performance. Collaborate with ML, product, and field engineering teams, document architectures, and mentor engineers.

Lead a multi-country team of solution architects and consultants to drive AI transformation and scaled adoption across EMEA. Shape strategy, governance, operating models, workforce readiness, and AI operationalisation; partner with sales to win opportunities, ensure delivery excellence, and develop scalable frameworks and high-performing teams.

Provide technical support for ClickHouse and Langfuse customers across EMEA and global regions through cases, chat, Slack, community channels, and calls. Troubleshoot database, cloud infrastructure, AI application, tracing, and evaluation issues within SLA and on-call rotations. Develop documentation and educational content, assist with technical proofs of concept, mentor colleagues, escalate bugs, collaborate with Engineering and Product teams, and support customer implementations directly in codebases.

Build scalable, resilient, and secure backend microservices and APIs for production AI applications across AWS and Azure. Integrate databases, third-party services, legacy platforms, and AI systems; optimize performance for real-time workloads; implement authentication, authorization, RBAC, and rate limiting; and apply testing, CI/CD, Git, code review, and containerization best practices.

Build responsive, high-performance frontend applications for AI-driven enterprise solutions. Develop reusable components using modern frameworks, integrate REST APIs, WebSockets, and real-time streaming responses, optimize performance and state management, apply WCAG accessibility standards, write component-level tests, establish frontend engineering patterns, and collaborate with UX, backend, platform, and AI engineering teams.

Design and maintain automated testing strategies for AI-enabled applications across frontend, backend, APIs, data flows, and integrations. Build automation suites, validate LLM prompts and outputs, perform integration, regression, end-to-end, performance, and load testing, investigate defects, support root-cause analysis, and integrate quality standards and release gates with development and platform teams.

Designs end-to-end enterprise AI architectures on AWS, including LLM orchestration, integrations, cloud infrastructure, security, scalability, and governance. Leads technical discovery workshops, creates architecture documentation and standards, guides engineering squads, reviews designs, presents decisions to senior stakeholders, and evaluates third-party AI platforms for buy-versus-build decisions. Requires extensive senior architecture experience and fluent Dutch and English.