Top Artificial Intelligence Jobs in France

Build and operate production AI products, including sales acceleration systems, recommendation engines, and agentic operating systems. Own solutions end to end by gathering user needs, designing, coding, deploying, monitoring, and maintaining them. Evaluate agent decisions, detect performance drift, enable non-engineers to create automations, and contribute to shared AI infrastructure. Work across TypeScript, Rust, Ruby, Google Cloud, Kubernetes, and infrastructure as code while collaborating across advisory, sales, operations, and engineering.

Build and optimize contributor acquisition campaigns for AI projects. Translate project needs into job ads, sourcing strategies, outreach, and funnel workflows; source niche and multilingual contributors; improve onboarding and activation; track conversion, retention, and acquisition-cost metrics; run experiments; and create repeatable operational playbooks. This is a hands-on individual contributor role combining growth operations, direct sourcing, campaign execution, and contributor experience improvement.

Designs and delivers AI-native solutions across business functions including People, Legal, Finance, and Operations. Responsibilities include identifying transformation opportunities, building and deploying AI agents and automated workflows, integrating enterprise systems, driving adoption, measuring business impact, applying governance and human oversight, and mentoring colleagues. The role partners with functional leaders to redesign operating models and continuously improve production AI solutions.

Own quality for AI agent systems through automated test frameworks, evaluation harnesses, regression suites, CI/CD quality gates, simulators, and API, integration, performance, and reliability testing. Perform exploratory testing, trace review, manual red-teaming, release verification, and UAT support. Build datasets, metrics, rubrics, dashboards, and release criteria while investigating failures and mentoring engineers on testing probabilistic LLM systems.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.

Architects and delivers enterprise AI, machine learning, generative AI, RAG, and agentic systems. Designs secure end-to-end architectures, integrates Claude and other models with ServiceNow, APIs, databases, cloud platforms, and workflows, and leads discovery, proofs of concept, and client delivery. Develops ML and NLP solutions, data pipelines, dashboards, evaluation frameworks, observability, governance, and responsible AI controls. Collaborates with stakeholders, engineers, consultants, and clients to operationalize reliable AI applications.

Architect and scale AI tool evaluation across NXP business lines, integrating emerging AI safety standards into development processes. Apply model validation, robustness testing, fault injection, interpretability, alignment, and runtime safety techniques to corporate AI tools, including LLM applications. Support safety and AI innovation projects, provide cross-functional technical guidance, and deliver recommendations for the AI and safety roadmap.

Lead and manage an Applied AI and Data Science team while remaining hands-on. Own delivery, architecture, hiring, mentoring, and production quality for generative AI, agentic AI, NLP, predictive analytics, and machine learning systems. Establish standards for evaluation, security, observability, cost, compliance, and AI-native software development. Partner with product, engineering, security, legal, and business stakeholders to deliver reliable, scalable AI platforms and reusable capabilities.

Lead Nuitee’s agentic AI strategy and capabilities for searching, booking, paying for, modifying, and canceling hotel stays. The role combines hands-on software engineering, product development, technical leadership, commercial opportunity identification, and team building. Reporting to the CEO, this leader will design secure agent-native APIs and MCP tools, improve transaction reliability, and take new products from concept to production across Nuitee’s travel infrastructure.

Build a model enablement platform for heterogeneous AI computing across AIPUs, CPUs, GPUs, NPUs, and other accelerators. Responsibilities include model ingestion, graph partitioning, compilation, runtime execution, performance profiling, memory and scheduling optimization, large-model offloading, and verification integrated into CI. The role combines architecture ownership with hands-on C++ and Python engineering across model frontends, compilers, runtimes, and application pipelines.

Leads EMEA AI, paid media, digital marketing, website, virtual event, email, and nurture strategies. Manages four direct reports, agencies, budgets, forecasting, campaign planning, AI marketing adoption, localized digital experiences, GEO initiatives, and performance optimization. Partners with global and regional stakeholders to improve engagement, conversion, pipeline contribution, and customer lifecycle outcomes.

Lead a small engineering team building and scaling production agentic AI and LLMOps systems. Design LLM orchestration, tool calling, MCP, RAG, evaluation, observability, and governance standards. Own a Databricks and MLflow-based ML platform, deploy open-weight models on Azure Kubernetes Service, optimize inference, and collaborate with product, data science, security, SRE, and cloud operations teams. Coach engineers while remaining hands-on with coding, architecture, prototyping, and technical initiatives.

Own Photoroom’s AI platform, including the AI gateway, safeguards, evaluations, cost controls, observability, no-code tooling, and agentic workflows. Prioritize platform capabilities for internal product teams, partner with engineering and ML research, translate model advances into reusable products, and use adoption, latency, cost, and evaluation data to guide decisions. The role requires strong product judgment, autonomous execution, platform experience, and production experience shipping agentic or model-backed products.

Develop and execute enterprise AI and data strategies for clients, including AI value cases, operating models, talent strategies, technology roadmaps, responsible AI plans, and commercialization opportunities. Build trusted C-suite relationships, lead transformation workstreams, shape new business opportunities, and guide large-scale AI-enabled reinvention initiatives. The role requires consulting experience, strong program management and communication skills, leadership capability, and up to 80% travel.

Lead the AI architecture for Productboard Insights, designing agent-native systems, context infrastructure, self-healing workflows, evaluation processes, and AI code-review optimization. Own technical direction across Insights, Spark, and platform teams; drive features from concept through beta; optimize quality, latency, cost, and reliability; write production code; and develop engineers through documentation and reviews.

Build practical prototypes for AI verification, including distinguishing GPU training from inference workloads and exploring zero-knowledge proofs and privacy-preserving evaluations. The role spans ML engineering, inference pipelines, algorithms, frontend demonstrations, cybersecurity, and collaboration with external experts. It also involves technical communications and engagement with policymakers and international partners in an ambiguous, early-stage environment.

Build and own AI models, data pipelines, evaluation suites, and AI product features for construction procurement software. Develop document extraction and workflow automation using generative AI, LLMs, RAG, and optimization algorithms. Design complex data models, integrations, backend services, and production systems using Go, GraphQL, PostgreSQL, and AWS.

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.

Develop and manage B2B content for priority industries, including white papers, reports, customer stories, articles, landing pages, and campaign assets. Drive SEO visibility, content distribution, repurposing, acquisition, and conversion performance. Collaborate with field marketing, SEO, lifecycle, social, PR, sales, and subject-matter experts while managing content projects end to end and optimizing performance against KPIs.

Build and optimize distributed ML training infrastructure for humanoid robot foundation models across thousands of GPUs. Responsibilities include large-scale pretraining, RL and post-training systems, GPU and memory optimization, accelerated kernels, data movement, numerical debugging, deterministic execution, checkpointing, fault tolerance, observability, and reproducibility. The role partners closely with AI researchers to convert experimental code into reliable, scalable training systems.

Provides strategic legal counsel to the Tagetik business unit in France, handling complex software transactions, services agreements, partnerships, vendors, and consulting arrangements. Advises business, product, engineering, and technology leaders on software, data, AI-enabled products, emerging AI regulations, and responsible AI frameworks. Requires strong drafting, negotiation, legal analysis, stakeholder influence, and change management skills, plus French and English fluency and willingness to travel.

Manages revenue excellence initiatives focused on process optimization, standardization, technology adoption, and AI-powered revenue management tools. Partners with revenue, commercial, finance, digital, and technology teams to design, deploy, and improve AI-assisted workflows. Drives change management, revenue culture transformation, data-driven decision-making, and adoption of global revenue processes across hotel operations.

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.

Own Mendo’s AI roadmap and technical direction while remaining hands-on with model training, fine-tuning, evaluation, RAG, embeddings, MLOps, and production operations. Establish AI quality and engineering standards across squads, optimize cost, latency, and performance, mentor engineers, and help grow the AI practice. The role focuses on proprietary on-device language models, production AI features, and enterprise-scale GenAI systems.