مطور ذكاء اصطناعي / بايثون

AI Development & Engine: Design and develop robust data pipelines and backend services in Python (FastAPI / Flask). Implement RAG (Retrieval-Augmented Generation) architectures for querying business document databases. Ensure integration, lightweight fine-tuning, or prompt engineering on LLMs (proprietary via APIs such as OpenAI/Azure or open-source such as Llama/Mistral).

Databases & Vectorization: Implement and optimize vector indexing using Vector Databases (ChromaDB, Qdrant, Pinecone, or PGVector). Process and prepare heterogeneous data (PDF documents, emails, text streams) for ingestion into AI pipelines.

Production Deployment & Industrialization (MLOps / DevOps): Containerize applications (Docker) and participate in deployment to Cloud/On-Premise environments. Create high-performance REST APIs to expose AI functionalities to business applications (member portal, CRM, management tools). Secure data usage (GDPR compliance, management of health data confidentiality).

Desired Candidate Profile

Essential Hard Skills: Languages ​​& Frameworks: Expertise in Python (3.10+), mastery of FastAPI or Flask, and AI integration libraries (LangChain, LlamaIndex). AI & LLM: Solid understanding of Transformer operation, the Hugging Face ecosystem, vector embeddings, and RAG mechanisms. Databases: Experience with Vector DBs (Qdrant, ChromaDB, PGVector) + standard SQL (PostgreSQL). Software Practices: Clean Code, unit/integration testing, version control (Git), containerization (Docker).

Soft Skills & Posture: Product & Business Awareness: Ability to translate a business need (e.g., time savings for claims/healthcare managers) into concrete AI functionality. Rigor & Autonomy: Focus on optimization (response time, LLM API cost management). Team Spirit: Ability to work in synergy with Data Engineers, Architects, and Product Owners using Agile methodology.