Das ist der Job
Applicants will need 2-3 years of professional experience in AI/ML Engineering or a closely related role.
Darum lohnt es sich
You will build and optimize RAG (Retrieval-Augmented Generation) pipelines while working with the ML Engineering team on model evaluation, testing, and continuous improvement. Work with the ML Engineering team on model evaluation, testing, and continuous improvement.
ENVIRONMENT
Design, develop, and deploy Agentic AI systems and LLM-powered applications in production environments as the next Junior-Mid Agentic AI Engineer wanted by a provider of cutting-edge Tech Applications.
At least one production-level Agentic project — you've built, deployed, and maintained an agent-based system that serves real users or real workloads. You will also require practical experience with RAG architecture & LLM application development.
DUTIES Design, develop, and deploy agentic AI systems and LLM-powered applications in production environments. Build and optimize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategies, embedding models, and retrieval mechanisms.
Integrate and manage vector databases (e.g., Pinecone, Weaviate, Qdrant, Milvus, ChromaDB) for efficient similarity search and knowledge retrieval. Develop and maintain Backend services and APIs (primarily in Python) to serve AI models and agent workflows. Contribute to the design of agentic architectures,
