Top Artificial Intelligence Jobs in Lyon

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 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.

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.

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.

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

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.

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 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.

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.

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.

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.

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.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.