MP DATA

Company Name: MP DATA
Job Title: Machine Learning Engineer – Industrialisation des modèles (H/F)
Job Description: **Job Title:** Machine Learning Engineer – Model Industrialization (MLOps) **Role Summary:** Design, develop, and operationalize machine‑learning models into scalable, production‑grade services. Build and optimise data ingestion, feature engineering, and training pipelines; implement CI/CD, containerisation, and observability solutions for ML workflows. Collaborate with data scientists, data engineers, and IT teams to translate proof‑of‑concepts into robust, cloud‑native deployments. **Expectations:** * Minimum 3 years’ professional experience in ML Engineering, MLOps, or ML‑oriented Data Engineering. * Proven ability to move prototypes into reliable, production‑ready systems. * Strong ownership and product mindset, delivering end‑to‑end solutions autonomously. **Key Responsibilities:** * Develop and maintain data pipelines and training workflows for large‑scale ML projects. * Implement CI/CD pipelines (GitOps, automated tests, model versioning) for continuous model delivery. * Containerise models using Docker; orchestrate deployments with Kubernetes and workflow tools such as Airflow or Kubeflow. * Deploy models as micro‑services (RESTful APIs) on cloud platforms; monitor performance, latency, and drift. * Ensure model observability: logging, metrics, alerting, and rollback mechanisms. * Work closely with data scientists to productionize feature stores and data platforms. * Continuously improve deployment architecture for scalability, reliability, and cost efficiency. **Required Skills:** * Programming: Python (scikit‑learn, TensorFlow, PyTorch). * MLOps tooling: CI/CD pipelines, Docker, Kubernetes, Airflow/Kubeflow. * Cloud & Data Platforms: AWS/GCP/Azure, data lakes, warehouses (Snowflake preferred). * DevOps/DataOps: version control, infrastructure‑as‑code, monitoring, observability. * Model versioning, lineage, and auditability. * Strong problem‑solving, communication, and collaboration abilities. **Required Education & Certifications:** * Engineer‑level degree (Bachelor/Master) in Computer Science, Engineering, or related field. * Certifications in cloud platforms (AWS, Azure, GCP) or MLOps (e.g., MLflow, TensorFlow Extended) are advantageous.

Paris, France

Hybrid

Junior

23-12-2025

Company Name: MP DATA
Job Title: Data Scientist NLP/AI Agents (5 ans XP)
Job Description: **Job title:** Data Scientist – NLP/AI Agents (5+ years experience) **Role Summary:** Lead the ideation, development, and industrialization of generative AI and autonomous agent solutions for a large industrial client. Drive the full lifecycle from concept to production, collaborating with data engineering, project management, and business stakeholders to deliver scalable, high‑performance AI services. **Expectations:** - Deliver end‑to‑end generative AI solutions within a strategic project for a major client. - Ensure robust, scalable, and maintainable production deployments. - Continuously innovate and integrate cutting‑edge generative models and agent frameworks. - Communicate technical concepts clearly to non‑technical stakeholders. **Key Responsibilities:** - Co‑design generative AI solutions (LLM, diffusion, multimodal, autonomous agents) tailored to business needs. - Prototype, fine‑tune, and deploy generative models, translating ideas into concrete use cases. - Define specifications, KPIs, and end‑to‑end AI pipelines with cross‑functional teams. - Manage model industrialization: robustness, scalability, maintenance, monitoring, and optimization. - Lead technology scouting, experimentation, and propose innovative methods to enhance projects. - Ensure ethical, secure, and regulatory compliance throughout the AI lifecycle. **Required Skills:** - Proficiency in Python and AI frameworks: PyTorch, TensorFlow, Hugging Face Transformers, Diffusers. - Hands‑on experience fine‑tuning LLMs, diffusion models, and multimodal architectures. - Expertise in MLOps: Docker, Kubernetes, MLflow, CI/CD pipelines. - Solid understanding of dialogue models, autonomous agents, and generative recommendation systems. - Knowledge of prompt engineering, embeddings, vector databases, and data governance best practices. - Strong communication, problem‑solving, and collaborative skills. - Awareness of AI ethics, security, and regulatory considerations. **Required Education & Certifications:** - Degree from an Engineering School or equivalent in Data Science, Artificial Intelligence, Computer Science, or related field. - Minimum 5 years of professional experience in Data Science/AI, with at least 2 years on generative AI or intelligent agent projects.

Boulogne-billancourt, France

Hybrid

Mid level

23-12-2025

Company Name: MP DATA
Job Title: Data Scientist NLP/LLM Engineer confirmé(e) (H/F)
Job Description: **Job Title** Senior NLP/LLM Engineer **Role Summary** Industrialise and deploy GenAI proofs‑of‑concept into scalable production solutions for industrial clients. Design robust LLM pipelines, RAG systems, and AI assistants, ensuring efficient integration with client IT and cloud environments. **Expectations** - Deliver production‑ready LLM services within client timelines. - Maintain high quality, scalable, and observable ML operations. - Collaborate cross‑functionally with data scientists, engineers, and IT teams. **Key Responsibilities** - Develop, fine‑tune, and optimise LLMs (LoRA, QLoRA, PEFT) for domain‑specific tasks. - Design and implement preprocessing, vectorisation, and generation pipelines. - Build and maintain RAG architectures and AI agents/assistants. - Deploy models to cloud (Azure, AWS, GCP) and handle integration with existing infrastructure. - Implement MLOps/LLMOps practices: CI/CD, Docker, Kubernetes, MLflow, versioning, monitoring, and alerting. - Work with vector databases (FAISS, Pinecone, Chroma) for retrieval and storage. - Conduct code reviews, unit tests, and documentation. **Required Skills** - Deep knowledge of LLM frameworks (OpenAI, HuggingFace, Mistral, Llama). - Proficiency in Python and libraries: transformers, LangChain, LlamaIndex, FastAPI. - Experience with RAG system design and vector database management. - Strong background in MLOps/LLMOps: CI/CD pipelines, Docker, Kubernetes, MLflow. - Cloud platform expertise (Azure, AWS, or GCP). - Familiarity with Snowflake (Snowpark, Cortex, vector search) is a plus. **Required Education & Certifications** - Graduate from a Grande École or equivalent engineering program. - Professional certifications in AI/ML or cloud platforms are advantageous.

Paris, France

Hybrid

05-01-2026

Company Name: MP DATA
Job Title: Senior data scientist (H/F)
Job Description: **Job title** Senior Data Scientist **Role summary** Lead the design, development, and production deployment of advanced probabilistic forecasting models to predict spare‑parts delivery windows for a European aeronautical industry leader. Transform complex, volatile logistics data into an intelligent, real‑time predictive engine that reduces delivery error rates and improves SLA compliance. **Expectations** - Deliver end‑to‑end forecasting solutions that directly impact contractual commitments. - Own model life‑cycle from experimentation to operational deployment, ensuring low‑latency inference for client consumption. - Communicate technical findings clearly to non‑technical stakeholders and collaborate across cross‑functional teams. **Key responsibilities** 1. Develop and tune predictive and probabilistic models (quantile regression, survival analysis, custom loss functions). 2. Build and optimize machine‑learning pipelines using XGBoost, LightGBM, CatBoost, and neural architectures such as Temporal Fusion Transformers. 3. Engineer high‑value features: cyclical time encoding, piece‑ID embeddings, backlog weighting, and other domain‑specific signals. 4. Quantify, prune, and otherwise optimize models to meet real‑time inference requirements. 5. Deploy models in production, implement monitoring, and maintain model health. 6. Produce interpretability outputs with SHAP or LIME and prepare stakeholder‑ready documentation. 7. Iterate on model design based on performance metrics and business feedback. **Required skills** - Advanced Python: Scikit‑learn, PyTorch/TensorFlow, Optuna, SHAP/LIME. - Expertise in XGBoost, LightGBM, CatBoost, Temporal Fusion Transformers, and survival‑analysis frameworks. - Strong feature engineering ability, including temporal encodings and embedding strategies. - Experience with custom loss function design and asymmetric penalty handling. - MLOps mindset: model versioning, monitoring, latency optimization (quantization, pruning). - Ability to explain complex models to business stakeholders. **Required education & certifications** - Bachelor’s or Master’s degree from a top engineering school (e.g., SupAero, Centrale, Mines) or equivalent in Engineering, Applied Mathematics, or Data Science. - Minimum 2 years of professional data‑science experience in an industrial setting, with a proven track record of deploying forecasting models that influence operational KPIs.

Balma, France

Hybrid

Senior

02-02-2026

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