Top Artificial Intelligence Jobs in France

Owns new-business sales across strategic accounts and assigned industry verticals. Responsibilities include territory and account planning, pipeline generation, consultative enterprise software selling, end-to-end opportunity management, RFI/RFP leadership, business-case development, partner collaboration, quota attainment, forecasting, CRM hygiene, and accurate sales reporting.

Leads enterprise FinOps AI and cloud engineering initiatives, defining agentic AI architectures, MCP integrations, policy-as-code, IaC guardrails, automation pipelines, security controls, telemetry, and executive dashboards. Manages multi-workstream, multi-cloud delivery squads, scales reusable FinOps components, supports client consulting and sales pursuits, and ensures compliance with enterprise AI standards. The role requires extensive cloud, FinOps automation, program leadership, and agentic AI experience.

Lead the engineering function on a remote consulting mission by setting technical direction, managing and mentoring engineers, driving the roadmap, overseeing complex delivery, improving engineering practices, and partnering with product and business stakeholders. The role requires hands-on technical leadership, architectural judgment, agile expertise, and evaluation of emerging technologies including cloud, AI, and blockchain.

Lead the engineering function for a growing technology company on a renewable consulting mission. Responsibilities include setting the engineering roadmap, managing and mentoring engineers, guiding architecture and technical decisions, leading complex projects, improving engineering practices, and collaborating with product and business stakeholders. The role requires hands-on technical involvement, strong software engineering expertise, and the ability to evaluate emerging technologies including cloud, AI, and blockchain. French fluency and strong English communication skills are mandatory.

Leads Thales AMS’s worldwide Data and AI transformation, defining the roadmap, governance, data foundations, engineering initiatives, AI agent marketplace, and AI-enabled process modernization. Oversees multidisciplinary AI and data engineering teams, drives enterprise AI deployments, develops talent, and advises senior stakeholders on long-term technology priorities. The role is based in Vélizy, France, involves international collaboration and approximately 30% travel, and requires occasional work outside standard hours.

Build and lead Quandela’s internal AI Ops department, including its AI platform, approved tools, integrations, automations, agents, evaluation, observability, cost management, and safety controls. Partner with business, legal, finance, hardware, and software teams to deliver reliable LLM-based workflows. Lead AI adoption through champion communities, training, office hours, and self-service tools while governing data access, sensitive information, and coding-agent guardrails.

Own mid-market sales growth across France, Belgium, and Luxembourg. Generate outbound pipeline, manage full-cycle B2B SaaS deals, build ROI-driven business cases, engage senior stakeholders, coordinate cross-functional deal teams, displace competing learning platforms, maintain CRM discipline, and deliver accurate forecasts. The role requires native French and fluent professional English, with experience selling SaaS, preferably LMS, LXP, HR technology, or workforce intelligence solutions.

Support the CEO, CTO, and Chief of Staff on high-priority company initiatives. Conduct market and competitive research, build US sales enablement materials, run growth experiments, develop finance reports and models, monitor healthcare AI regulation, support public affairs, and manage founder branding. The role requires independent execution, structured recommendations, and cross-functional collaboration across Paris and New York.

Design, build, deploy, and operate production AI-native systems spanning applications, APIs, data, cloud infrastructure, DevSecOps, security, observability, and agentic AI. Lead architecture, client engagements, multidisciplinary engineering teams, and technical delivery from ambiguous requirements through production. Build cloud-native platforms, CI/CD pipelines, secure data and context architectures, AI integrations, agent orchestration, evaluations, and production safeguards. Establish reusable engineering standards, tooling, and practices for enterprise AI adoption.

Build and scale the backend infrastructure powering Goodweek’s AI platform. Responsibilities include AI orchestration, LLM integration, RAG pipelines, data ingestion and export, context and token optimization, scalable APIs, security, observability, deployment automation, and testing. The role also contributes to architecture decisions, technical documentation, and engineering practices. Candidates need 5+ years with Node.js and TypeScript, backend architecture expertise, AI/ML integration experience, AWS, databases, authentication, CI/CD, and strong technical communication.

Ensure Pigment’s implementation methodology is consistently applied across integration partners. Responsibilities include gathering requirements, reviewing business and technical designs, validating configured models, assessing quality and accuracy, escalating issues, supporting partners with modeling and integrations, and helping develop GenAI-based QA tools. The role also involves customer and partner communication, technical analysis, certification, and continuous improvement.

Leads enterprise Celonis implementations by conducting discovery workshops, translating business needs into technical specifications, directing AI agents, building process models and operational solutions, validating outputs, defining KPIs, quantifying value, and enabling customer adoption. Develops domain expertise, manages project outcomes, trains users, documents solutions, and collaborates with stakeholders to improve business processes and shape scalable platform use cases.

Designs, develops, and operationalizes scalable AI and machine learning solutions for enterprise clients. Responsibilities include selecting and customizing deep learning and generative AI models, building data and MLOps/LLMOps pipelines, deploying and monitoring production systems, processing large datasets, researching high-performance computing applications, and collaborating with technical and business stakeholders. Client-facing consulting work and travel may be required.

Own France revenue across sales and partnerships, scale ARR from €5M to €30M within 18 months, and build the organization toward €40–50M+ ARR. Lead business-unit sales leads and partnerships, hire and coach teams, establish forecasting and pipeline discipline, design quotas and compensation, improve large-account closing, and represent revenue strategy on the executive committee. The role also partners with Customer Operations, Product, and Operations to improve the revenue engine and customer handoffs.

Provide broad French country legal support with a primary focus on French and EU regulatory and policy matters affecting OpenAI. Advise on legislation, enforcement, business implications, and regulatory engagement; prepare legal analyses, consultation responses, and briefing materials. Support local partnerships, research collaborations, marketing, events, governance, employment, workplace compliance, and office operations while coordinating with specialist legal and policy teams.

Develop and optimize generative AI applications for consulting clients across industries. Responsibilities include prompt engineering, RAG, semantic search, model selection and fine-tuning, multimodal AI, production monitoring, scalability, security, compliance, and responsible AI practices. The role collaborates with data scientists, platform engineers, architects, and customers to design bespoke solutions and evaluate emerging AI frameworks and techniques.

Own end-to-end delivery of AI and backend projects within the Incidents team. Design resilient, high-performance systems for processing millions of incidents and presence checks, bring LLM capabilities into production, and contribute to technical and product decisions. Review code, mentor engineers, improve engineering practices, participate in cross-team architecture discussions, and support deployments. The role uses Python, Django, Celery, distributed systems, and large-scale data processing.

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.