Top Artificial Intelligence Jobs in Paris

Join the Incidents Squad to build, scale, and stabilize LLM-powered features across the incident lifecycle. Implement agentic workflows, improve reliability, latency, robustness, observability, and productionize AI systems while partnering with ML engineers and shaping platform orchestration and performance.

Design and deploy production AI agents and specs-to-production pipelines to automate engineering tasks (bug triage, diagnostics, patch suggestions, code review). Build automated workflows from design to merge-ready code, improve test generation and AI-assisted validation, consolidate team AI practices, and co-own the AI toolchain roadmap while embedding with feature teams.

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.

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 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.

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 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.

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.

The Principal Machine Learning Engineer will lead cross-functional AI initiatives, define ML architecture, establish evaluation frameworks, and influence technical standards across teams.

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.

Work with the Threat Research team to apply generative AI and LLMs to fraud and threat detection: prototype multi-agent pipelines, build a GenAI cloud stack, integrate AI features into products, and collaborate with engineering teams to automate traffic analysis and generate detection summaries.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.