ML engineer confirmé (H/F)
Core
Design and develop scalable AI/ML architectures in public cloud, integrate models into applications, and deploy custom or managed models for clients.
Role type
Senior Machine Learning Engineer (Generative AI & MLOps)
Builds
Scalable AI/ML solutions, Generative AI systems (LLMs, RAG), and MLOps pipelines for public cloud clients
Domain
Consulting, Generative AI, Machine Learning, Cloud Infrastructure
Deliverable
production ML models | product features | infrastructure
Required skills
Python, Cloud Public (AWS/GCP/Azure), MLOps (MLflow/Kubeflow), LLMs & RAG, Vector Databases, Containerization (Docker/K8s), CI/CD (Terraform/GitLab), Model Monitoring & Evaluation
Preferred skills
Experience with SageMaker/Bedrock/Vertex AI, LangChain/LlamaIndex, FastAPI/Flask, Technical mentoring
Technologies
AWS, GCP, Azure, SageMaker, Bedrock, Vertex AI, MLflow, Kubeflow, Airflow, MWAA, Step Functions, Cloud Composer, Cloud Workflows, GitLab, Terraform, Docker, Kubernetes, ECS, EKS, Lambda, Cloud Run, GKS, Cloud Functions, Python, FastAPI, Flask, LangChain, LlamaIndex, PostgreSQL (pgvector), Elasticsearch, OpenSearch, Vertex AI Vector Search
Responsibilities
Design and develop scalable AI/ML architectures in public cloud; Integrate AI/ML models into existing applications; Set up and optimize MLOps pipelines; Implement model evaluation and monitoring approaches; Develop Generative AI solutions based on LLMs and RAG with vector databases; Deploy custom models or integrate managed models; Collaborate with cross-functional teams (Data Science, Data Engineering, DevOps, Business); Participate in technology scouting and AI innovation tracking; Educate clients on legal and ethical aspects of AI
Seniority
Senior, hands-on IC
