Top Artificial Intelligence Jobs in Paris

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.

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.

Own LLM and VLM enablement on Axelera AI hardware, from studying and profiling models through productionizing them in the Voyager SDK. Responsibilities include PyTorch references, quantization, weight mapping, model contracts, pipeline integration, KV-cache management, multimodal serving, accuracy validation, and bring-up platform development. The role also shapes GenAI, agent, and speech capabilities for edge devices.

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 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.

Drive Cisco growth through strategic partner relationships, joint go-to-market strategies, pipeline management, and revenue execution. Orchestrate technical and portfolio resources to deliver solutions, communicate complex value propositions, negotiate agreements, and expand Cisco’s AI ecosystem. The role requires understanding partner business models, customer needs, financial metrics, market dynamics, and AI’s business impact while managing multiple priorities in a fast-paced B2B environment.

Lead and coach a team of Outcomes Architects responsible for workflow adoption, value realization, renewals, expansion, and at-risk account recovery. Build operating cadences, inspect customer and commercial outcomes, hire and develop team members, manage performance, and partner with Sales, Solutions Consulting, Services, Support, Product, and RevOps. Ensure strong workflow, product, AI, and customer-outcome execution across the team.

Build and maintain backend services, APIs, integrations, evaluation systems, data pipelines, and development environments for AI agents. Develop automated evaluations, analyze agent traces and failures, support model integration, and improve service reliability through testing, monitoring, logging, and safe deployments. Collaborate with software engineers, researchers, model-training teams, data specialists, and product owners while writing maintainable Python code and owning scoped components through implementation and operation.

Leads GTM systems and AI strategy, including CRM and marketing automation architecture, AI agent development, vendor management, roadmap planning, KPI measurement, adoption, and change management. Manages and coaches five senior individual contributors across Salesforce, marketing, customer success systems, and web operations. Partners with commercial, data, enablement, security, and finance leaders to deliver scalable tools that improve efficiency, pipeline, conversion, and revenue.

Leads engineering strategy, organization, execution, and technical excellence across international teams. Partners with executive, product, data, and technology leaders to scale architecture, infrastructure, reliability, security, delivery practices, and engineering leadership. Builds and retains high-performing teams, improves velocity and predictability, connects technical investment to business outcomes, and prepares engineering for increased data, automation, and AI adoption.

Six-month procurement data and AI internship supporting AI solution deployment, business intelligence, data architecture, data governance, automation, and workshop-led adoption. The intern will translate procurement needs into technical solutions using Generative AI, Python, SQL, APIs, Power BI, and Google Workspace tools while documenting data flows and improving data quality. The role is based at Air Liquide’s headquarters in Paris and starts in January 2027.

The role involves performing data-related tasks supporting AI content improvement in French, including data collection, evaluation, annotation, and object tagging.

Lead Questel’s finance-focused AI transformation by identifying high-value opportunities, developing business cases, building lightweight AI prototypes and agents, measuring ROI, enabling adoption, training teams, maintaining an approved AI tools catalogue, and sharing reusable prompts and patterns. The role bridges business stakeholders and the central AI team, combining hands-on generative AI expertise with consulting, change management, and corporate finance experience.

Design and deploy production-scale AI systems, develop and optimize machine learning and neural network models, work with large language models, refine prompt engineering techniques, and use OpenAI and Anthropic APIs. Collaborate with interdisciplinary teams to build AI applications for the finance sector while keeping current with industry developments.

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.

Lead enterprise AI solution architecture across reusable capabilities, defining engineering patterns, integration designs, deployment strategies, security controls, monitoring, lifecycle management, and technical standards. Partner with application, platform, infrastructure, data, DevOps, security, and delivery teams. Contribute hands-on to proof-of-concepts and production AI solutions while guiding technology adoption and enterprise AI roadmap decisions.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Own the vision, roadmap, architecture, and hands-on delivery of IFS’s Internal Developer Platform. Build developer portals, service catalogs, self-service scaffolding, golden paths, CI/CD integrations, authentication, authorization, and observability workflows. Drive adoption through metrics, feedback, and developer research while partnering with infrastructure, IAM, SRE, and distributed product teams. The role requires production Kubernetes operations, enterprise-scale platform ownership, production AI application experience, and strong technical leadership without direct management responsibility.

Own end-to-end product design for MEDvidi’s AI Track, creating human-AI interaction patterns for clinical tools such as AI Scribe, Chart Review, and AI Receptionist. Gather requirements independently, research provider needs, prototype directly in code using Claude Code or Cursor, collaborate with product and engineering, contribute reusable Storybook components and design-system documentation, validate designs, and guide solutions through release.

Serve as the senior technical expert for EMEA AI organizations training and post-training large-scale foundation models. Advise customers on distributed training infrastructure, PyTorch, Megatron-LM, NeMo, reinforcement learning, GPU utilization, communication overlap, and memory management. Build strategic technical relationships, translate customer needs into NVIDIA product roadmap input, collaborate with research and product teams, and support hackathons, demonstrations, and technical conferences.

Build and deploy reliable, measurable, and safe AI agents for real business workflows. Work within Marketing Engineering and growth pods to identify problems, develop end-to-end solutions, evaluate LLM performance, and measure business impact. The role involves productionizing agent systems beyond demos, using Python, SQL, LLM APIs, and orchestration tools, with potential integration into go-to-market data and marketing systems.

Leads end-to-end architecture for enterprise data, analytics, and AI solutions in a Microsoft-first environment. Translates business requirements into secure, scalable designs and guides prototyping, implementation, architecture reviews, troubleshooting, governance, and production readiness. Requires deep hands-on experience with Azure data platforms, Power BI, enterprise security, Azure OpenAI, and retrieval-augmented generation patterns.