Über
We are seeking a hands-on Data Scientist/AI Engineer/ML Engineer to design, build, evaluate, and deploy customer-facing LLM applicationswith a primary focus on retrieval-augmented generation (RAG), agentic workflows, and production-grade Azure deployments. This role will be responsible for delivering a web-enabled, customer-facing chatbot that combines proprietary knowledge with live web search, integrates securely with enterprise systems, and meets high standards for accuracy, reliability, observability, and safety. This is not a research-only role. You will write production code, build evaluation harnesses, and own models and services from prototype through live deployment. Skills and Requirements: -4+ years of experience in Data Science, Machine Learning Engineering, or AI Engineering, with recent hands-on work in Generative AI / LLMs. -Strong proficiency in Python for production-grade ML and AI services. -Demonstrated experience building RAG-based LLM applications beyond simple demos or notebooks. -Hands-on experience with vector databases or vector search systems (e.g., Azure AI Search, Pinecone, FAISS, etc.). -Practical experience with prompt engineering, prompt chaining, and agent/tool orchestration. -Experience designing LLM evaluation frameworks and quality metricsnot just manual testing. Azure & Cloud Experience: -Production experience with Azure OpenAI and Azure-based AI services. -Experience deploying AI/ML services using Azure-native infrastructure (Functions, App Services, Containers, CI/CD). -Familiarity with observability and telemetry for AI systems (logging, metrics, tracing). LLM Application Engineering: -Experience integrating external tools, APIs, or web search into LLM workflows. -Understanding of LLM limitations, failure modes, and mitigation strategies. -Ability to design systems that balance accuracy, latency, cost, and safety. -Experience with LangChain, Semantic Kernel, LlamaIndex, or similar orchestration frameworks. -Experience with hybrid search (keyword + vector) and reranking strategies. -Familiarity with responsible AI, content filtering, and prompt safety patterns. -Experience building customer-facing chatbots or conversational AI systems at scale. -Background in NLP, information retrieval, or applied ML research.
Sprachkenntnisse
- English
Hinweis für Nutzer
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