Darum lohnt es sich
Responsibilities Design, develop, and deploy production models, services, and pipelines that are reliable, scalable, and maintainable Partner with data science, product, data engineering, and platform teams to translate business problems into technical solutions Build and optimize model training, evaluation, deployment, monitoring, and retraining workflows Monitor deployed models for performance degradation, bias, drift, and operational issues Mentor engineers and contribute to technical standards, best practices, and architecture decisions Requirements A university degree required (i.e.
Bachelors degree) or equivalent relevant work experience 5–10+ years of hands‑on experience in software, AI, or data engineering with strong Python proficiency Experience designing and delivering end‑to‑end AI workflows and applications Hands‑on experience with LLM/GenAI technologies Experience with AI/LLM frameworks and tools (e.g., LangChain, LlamaIndex, Semantic Kernel) Experience working with messy, unstructured enterprise data Strong engineering and product judgment including API/application development Hard Skills Python AI workflows Data engineering Model training Model evaluation Model deployment Model monitoring Model retraining API development Application development Soft Skills Mentoring Technical standards Best practices Architecture decisions Engineering judgment Product judgment Certifications & Qualifications Bachelor's degree Industry Keywords Production models Scalable services Reliable pipelines Performance monitoring Bias detection Data science Product teams Data engineering teams Enterprise data Unstructured data Tools & Technologies LLM technologies GenAI technologies LangChain LlamaIndex Semantic Kernel #J-18808-Ljbffr
