AI Engineer

• Automatiser les workflows métier entre nos différents outils
• Traiter les données en entrée de nos process
• Mettre à disposition des skills IA partagés, réutilisables par toutes les BU
• Faire grandir une communauté interne autour de l'utilisation de l'IA
• Concevoir et déployer des workflows automatisés sur n8n ou équivalent, connectant nos outils métier
• Identifier les tâches répétitives à fort impact et les transformer en flux autonomes
• Concevoir et maintenir des pipelines d'extraction et de structuration de documents métier
• Travailler en lien avec les data engineers pour intégrer ces outputs dans les pipelines de données du groupe
• Construire une bibliothèque de skills et d'agents IA réutilisables
• Animer une communauté interne de pratiques autour de l'IA

AI Ops Engineer – F/H

• Defining the long-term vision and technical strategy for our GenAI Platform
• Building the infrastructure that powers image generation, video generation, model serving, orchestration, evaluation and agentic workflows
• Partnering with Research to rapidly bring new models into production
• Driving platform reliability, scalability and cost efficiency across billions of inference requests
• Leading and growing a multidisciplinary team of ML Engineers, Software Engineers and Researchers
• Creating an environment where ambitious technical ideas can move from research to production in days rather than months
• Working closely with Product, Engineering and Executive leadership to ensure platform investments directly accelerate product innovation

Head of GenAI Platform

• Piloter la stratégie IA du projet : expérimenter, tester et industrialiser l'usage des LLMs
• Garantir la qualité logicielle : concevoir des architectures robustes et itérer rapidement
• Implémenter des agents IA pour développer des fonctionnalités innovantes et améliorer les pratiques de développement
• Travailler de manière itérative, livrer régulièrement de la valeur aux utilisateurs et mettre en place une boucle de feedback courte

Consultant, AI Software Engineer, SDLC

• Design and develop AI solutions in Python.
• Implement Retrieval-Augmented Generation (RAG) pipelines to improve model relevance.
• Leverage the AWS cloud, particularly AI/LLM services (e.g., Bedrock).
• Integrate AI tools and orchestration platforms (N8N, OpenAI, Claude, …).
• Conduct experiments and drive continuous improvement through prompt engineering and model fine-tuning.
• Monitor AI technology trends and propose ideas/concepts to enhance our products.
• Collaborate with product, data, and engineering teams to industrialize reliable, scalable AI solutions.

Experienced AI Developer

• AI is central to 360Learning's strategy: our AI Squad ships agentic features that help users discover, create, and administrate content.
• Build a global view of our codebase and architecture (Pydantic AI, FastAPI, Azure OpenAI, ElasticSearch)
• Contribute meaningfully to an existing feature, hands-on
• Coach our team of AI Engineers (2 today, growing)
• Own end-to-end delivery of a new AI feature, from technical design through production launch
• Optimize for real-world constraints: reduce per-request cost, improve latency, and scale to handle usage spikes
• Set the technical direction for AI features alongside the PM's product vision
• Push technical innovation within the squad and across R&D (e.g. hackathons, transversal initiatives) while keeping roughly 80% hands-on and ~20% on coaching and management

Lead AI Engineer

• Design and development of AI solutions in Python
• Implementation of RAG (Retrieval Augmented Generation) pipelines to improve model relevance
• Utilization of the AWS cloud, with a focus on AI/LLM services (e.g., Bedrock)
• Integration of AI tools and orchestration platforms (N8N, OpenAI, Claude, ...)
• Experimentation and continuous improvement through prompt engineering and model fine-tuning
• Keeping up-to-date with AI innovations and proposing ideas/concepts to enrich our products
• Collaboration with product, data and development teams to industrialize reliable, scalable AI solutions

Senior AI Developer

Machine Learning Engineer

• Own ML models across their full lifecycle - from data pipelines and feature engineering to training, evaluation, deployment and monitoring; choosing the right metrics and guarding against leakage, overfitting and drift.
• Run and improve our ML platform - own the team's GitOps CI/CD and release process, monitor serving endpoints, latency and load in Datadog, and define the SLOs and alerting that keep models reliable in production.
• Turn ML into business value across the org - collaborate with risk, operational and product teams to spot and ship ML opportunities, from credit scoring to AI for operational intelligence, sharing your expertise through code reviews and tech watch.

ML Engineer

• The AI Engineer designs, deploys and industrializes generative AI, machine learning and automation solutions to support various business functions (education/pedagogy, learner relations, internal teams)
• Transform concrete business needs into reliable, measurable and controlled AI products (costs, latency, compliance), from scoping through production
• Define and scope requirements expressed by different teams, estimate ROI and make build-vs-buy and implementation trade-offs
• Design and industrialize generative AI and ML solutions: RAG (retrieval-augmented generation), educational and administrative chatbots, agents, automations
• Document, harden and evolve solutions (CI/CD, Dev/Staging/Prod environments)

• Design and implement AI solutions using Large Language Models (LLMs), retrieval-augmented generation (RAG), and agentic frameworks to address complex business challenges
• Build and maintain information retrieval pipelines, including hybrid search (sparse + dense), vector databases, and multi-stage re-ranking systems
• Develop and fine-tune LLMs and vision-language models (VLMs) for domain-specific tasks such as product categorization, attribute extraction, and semantic search
• Build and orchestrate agentic AI systems that interact with internal tools and external data sources
• Write production-ready code and deploy AI systems at scale in live environments
• Define and track evaluation metrics aligned with product and business goals; run controlled experiments (A/B tests, benchmarks) and clearly communicate results
• Collaborate with software engineers, product managers, and business stakeholders to frame problems from both scientific and business perspectives
• Investigate and resolve production issues; ensure reliability, observability, and performance of AI systems
• Keep up to date with the latest AI trends (Generative AI, agentic systems, scalable ML, etc.)

• Train and fine-tune large-scale multimodal models (vision-language, audio, speech) from scratch and from pretrained checkpoints
• Extend models across modalities: image understanding, video temporal modeling, long-context processing, and streaming audio
• Design and run experiments: architecture changes, data mixes, training recipes
• Build and maintain multimodal data pipelines — from raw images, video, and audio recordings to training-ready datasets, including synthetic data generation
• Train and optimize MoE architectures for efficient multimodal inference
• Build alignment pipelines: SFT, DPO, GRPO, reward modeling — across modalities, not just text
• Optimize models for production: quantization, distillation, batching, streaming and low-latency serving
• Deploy models end-to-end: from research checkpoint to production serving
• Define evaluation metrics and benchmarks that actually matter for the product: visual QA, spatial reasoning, video comprehension, speech and audio understanding

Multimodal ML Engineer

• Design and develop AI solutions in Python.
• Implement RAG (Retrieval-Augmented Generation) pipelines to improve model relevance.
• Leverage AWS cloud services, in particular AI/LLM services (e.g., Bedrock).
• Integrate AI tools and orchestration (N8N, OpenAI, Claude, etc.).
• Experiment and continuously improve via prompt engineering and model fine-tuning.
• Monitor AI technology trends and propose ideas/concepts to enhance our products.
• Collaborate with product, data, and development teams to productionize reliable, scalable AI solutions.

• Conception et développement de solutions IA en Python.
• Mise en place de pipelines RAG (Retrieval Augmented Generation).
• Exploitation du cloud AWS et en particulier des services IA/LLM (ex. Bedrock).
• Intégration d’outils IA et d’orchestration (N8N, OpenAI, Claude, …).
• Collaboration avec les équipes produit, data et dev.
• Veille technologique sur les innovations IA et proposition d’idées/concepts.

Développeur confirmé, IA

• Map current-state workflows end-to-end to diagnose root causes of operational friction, including handoff failures, governance gaps, and tooling mismatches.
• Redesign target-state operations for leaner workflows, clearer decision rights, and effective human-agent collaboration to accelerate throughput using AI.
• Design comprehensive operating models, including organizational structure, roles, governance, cross-functional interaction models, and value realization.
• Establish performance baselines and build measurement frameworks that track improvement continuously throughout the engagement.
• Identify value gaps in stalled implementations, diagnosing where technology deployment has outpaced business adoption to restore the path to ROI.
• Build working, AI-powered prototypes directly utilizing LLM APIs, platform-native features, and low-code/no-code environments to create functional solutions.
• Utilize working demos and prototypes as the primary tool for stakeholder alignment directly within the client's actual processes.
• Evaluate where AI practically enhances a workflow versus where it serves as a distraction, advising clients with realistic, grounded expectations.
• Support the rollout of new tools and workflows, ensuring adoption is rooted in process design and change management.
• Design enablement materials that ensure new technology implementations effectively connect to and support the intended operating model.
• Support complex engagement scoping and conduct diagnostic assessments for value capture, implementation readiness, and adoption strategy.
• Contribute reusable frameworks and tools that continuously improve practice qualification and scoping.

Forward Deployed Engineer – AI, Business Transformation

• Map creative workflows end-to-end, including briefing, concepting, production, review, localization, activation, and measurement.
• Identify operational bottlenecks, inefficiencies, and opportunities for automation.
• Diagnose root causes of workflow friction, including approval delays, handoff failures, tooling gaps, and governance challenges.
• Design future-state workflows that effectively balance AI automation and human oversight.
• Configure and implement creative AI platforms and tools against real production requirements.
• Integrate AI-powered workflows into existing ecosystems, including DAMs, CMS platforms, marketing technology stacks, and project management systems.
• Build prompt architectures, agent workflows, automation layers, and quality control mechanisms that can operate reliably at production scale.
• Develop working proofs of concept and production-ready solutions that demonstrate measurable business value.
• Rapidly iterate and deploy solutions within the constraints of active client engagements.
• Establish governance frameworks covering brand safety, content rights management, model selection, approval workflows, compliance, and auditability.
• Train and enable teams to successfully adopt new workflows and technologies.
• Define and track success metrics, including throughput, cycle time, cost-per-asset, quality consistency, and operational efficiency.
• Ensure knowledge transfer and long-term sustainability beyond the initial engagement.
• Contribute to project scoping and solution design across content operations, creative production, AI transformation, and marketing technology initiatives.
• Develop reusable accelerators, templates, tooling configurations, and best practices.
• Help evolve internal methodologies and delivery frameworks for future engagements.

Forward Deployed Engineer – AI, Creative Transformation

• Continue to develop, productize and maintain a business critical internal application for HPC/AI cluster design and simulation having dependencies on other internal critical applications.
• Use AI-assisted development tools (Claude, Gemini, etc.) to accelerate coding, prototyping, and problem-solving
• Participate in the full software lifecycle: design, implementation, testing, deployment, and maintenance
• Collaborate with HPC, infrastructure, and AI stakeholders to translate requirements into technical solutions
• Ensure performance, scalability, and reliability of the application
• Improve and automate development workflows using CI/CD best practices
• Contribute to code reviews, documentation, and knowledge sharing
• Continuously explore new tools, frameworks, and approaches to enhance productivity and product quality while maintaining production grade standards.

AI-Driven Application Developer – HPC/AI Tooling

• Design machine learning solutions and tools across the full ML lifecycle
• Contribute to all areas of our data platform
• Work closely with other machine learning engineers and data engineers

Senior Machine Learning Engineer

• Integrate new text and multimodal models into our serving paths and verify they behave correctly under production-like traffic.
• Build and maintain rollout pipelines for frequent model releases.
• Create smoke, quality, and performance gates for model promotion.
• Operate local and cluster GPU deployments on Kubernetes.
• Build dashboards for latency, throughput, queue depth, GPU usage, fallback rate, and quality drift.
• Run A/B and canary rollouts for model, prompt, routing, and serving config changes.
• Debug production issues across model config, tokenizer, serving API, router, queue, Kubernetes, GPU runtime, and CI jobs.
• Optimize serving cost and reliability across mixed GPU capacity.

MLOps Engineer

AI Research Scientist

• Own research projects end to end — from an unclear concern ("how do we even define sloppy research outputs?") to a falsifiable experiment, clean baselines, and a result you can defend.
• Develop automated audit agents that discover and characterise suspect model behaviour at scale.
• Study how misalignment and bias actually show up when real users interact with agents, and turn what you find into evals our products can ship.
• Pressure-test frontier agents in realistic, high-stakes scenarios to find where they break before our customers do.
• Run white-box and block-box investigations to understand how AI models fail.
• Publish what you learn as public blog posts and conference papers, and feed the rest back into our internal guardrails.

Research Scientist – AI Behaviours

• Design and build internal AI agents that automate workflows across teams such as Sales, HR, Customer Operations, and Finance
• Develop AI-powered tools and assistants that help Pelico teams work faster and make better decisions
• Build end-to-end AI workflows connecting LLM APIs, internal data sources, and operational systems
• Develop backend services and APIs (Python, FastAPI or similar) to support AI-driven internal tools
• Prototype and iterate quickly, shipping small AI applications that solve real operational problems
• Integrate AI systems with internal tools such as Slack, Notion, CRM systems, or internal databases
• Improve and maintain existing AI systems, debugging workflows and optimizing performance when things break
• Collaborate with internal teams to identify opportunities for automation and AI augmentation
• Contribute to Pelico’s internal AI stack, helping define best practices for building and deploying AI-powered tools

AI Ops Engineer

• Lancer et développer les activités commerciales sur le marché français
• Identifier, qualifier et conclure de nouvelles opportunités commerciales
• Développer et entretenir des relations solides avec les acteurs clés du secteur
• Gérer l’ensemble du cycle de vente, de la prospection à la signature des contrats
• Accompagner l’onboarding des nouveaux clients
• Représenter l’entreprise lors d’événements professionnels

Machine Learning Engineer jobs in France

See all jobs on JobTailor

AI Ops Engineer – F/H

Head of GenAI Platform

Consultant, AI Software Engineer, SDLC

Experienced AI Developer

Lead AI Engineer

Senior AI Developer

ML Engineer

AI Engineer

Lead AI Engineer

Multimodal ML Engineer

Senior AI Developer

Développeur confirmé, IA

Forward Deployed Engineer – AI, Business Transformation

Forward Deployed Engineer – AI, Creative Transformation

AI-Driven Application Developer – HPC/AI Tooling

Senior Machine Learning Engineer

MLOps Engineer

Research Scientist – AI Behaviours

AI Ops Engineer

Responsable Commercial – IA en Radiologie, Imagerie Médicale