Top Artificial Intelligence Jobs in Lyon

Lead design and delivery of AI-powered visualization and investigation features using LLMs. Build reliable, explainable systems combining deterministic product capabilities with generative models, drive prompt/context engineering, evaluation frameworks, and cross-team product-focused technical leadership and mentorship.

Design and deliver AI-powered experiences for Datadog Notebooks. Build reliable backend services integrating LLMs and AI agents, manage model nondeterminism with evaluation/testing frameworks, partner with product/design, provide technical leadership, and operate production systems (on-call).

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, including LLM inference infrastructure, RAG, AI agents, and evaluation/observability. Design and run experiments (A/B tests), optimize systems for production performance and reliability, and iterate rapidly with product and design partners.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

Rotational graduate program within Alan's Insurance team covering enterprise offers, insurance product lifecycle, analytics and repricing, and reinsurance/risk. After a two-month onboarding you'll work cross-functionally with Engineering, Data, Finance and Marketing to build prevention-focused insurance products, leveraging SQL and AI, with coaching and visible impact from day one.

Drive development and deployment of AI solutions, translating business needs into product requirements, managing backlogs, and coordinating cross-functional teams.

Lead business analytics projects partnering with ABB business areas: design data migration, integration, validation, reporting and governance; manage cloud analytics platforms, vendors and infrastructure; pilot AI/ML solutions and ensure alignment with business objectives and controls.

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.

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 global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Act as primary technical contact in pre-sales for large-scale GPU and AI projects: analyze AI workloads, design and provision custom GPU clusters, run POCs, translate constraints into business value, and collaborate with sales and engineering to close enterprise deals.

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.

Build end-to-end AI features and prototypes across backend and frontend, prototype AI agent use cases, create UI/blueprints/examples to make AI workflows usable, and ship features quickly in collaboration with product and backend teams.

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.

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Senior Director analyst providing thought leadership and actionable research on infrastructure and operations AI strategy. Produce insights, presentations, and client advisories; guide AI infrastructure, cloud adoption, integration, governance, and vendor selection. Lead research agenda, mentor analysts, collaborate cross-functionally (infrastructure, engineering, enterprise architecture, cybersecurity), support sales, and represent Gartner at events. Drive process improvements and influence senior stakeholders to align infrastructure and operations with business outcomes.

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.

Senior Product Lead to define, roadmap, and deliver Agentic AI solutions for enterprise programs. Lead requirements gathering, shape end-to-end AI/ML solutions, create PRDs and success metrics, coordinate cross-functional teams, manage stakeholders, and drive adoption, training, and value realization across brands.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

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.