Stellenbeschreibungph3Responsibilities /h3ulliDesign and implement real‑world agentic AI systems using modern agent frameworks and orchestration tools. /liliDevelop agentic workflows that go beyond chat, including complex analytical pipelines, multi‑step research workflows, tool‑using agents, knowledge‑grounded agents, and structured decision‑support systems. /liliWork with knowledge‑based AI architectures, including retrieval‑augmented generation, knowledge graphs, symbolic rules, structured domain models, ontologies, and hybrid reasoning systems. /liliDevelop and apply mechanisms for controlling inference, including planning constraints, reasoning policies, guardrails, validation layers, tool‑use control, and human‑in‑the‑loop checkpoints. /liliExplore and implement neuro‑symbolic approaches for agentic reasoning, combining LLM‑based reasoning with symbolic, rule‑based, graph‑based, or formally structured methods. /liliBuild transparent AI methods that make agent behaviour traceable, explainable, testable, and auditable. /liliCreate evaluation and testing frameworks for agentic systems, including benchmark tasks, regression tests, failure‑mode analysis, trace inspection, robustness testing, and task‑level performance measurement. /liliDevelop full‑stack prototypes and production applications, integrating backend services, APIs, databases, frontend interfaces, model providers, and orchestration layers. /liliCollaborate with researchers, engineers, product teams, and domain experts to translate ambiguous real‑world problems into reliable agentic workflows. /liliStay current with developments in agentic AI, reasoning systems, LLM orchestration, AI evaluation, and applied neuro‑symbolic methods. /li /ulh3Requirements /h3ulliMust have neuro symbolic reasoning experience. /liliStrong multi‑project experience developing real‑world AI agents or agentic workflows. /liliDemonstrated focus on agentic reasoning, including planning, decomposition, tool use, multi‑step inference, workflow execution, or autonomous task completion. /liliExperience in either industrial AI development, academic research, or ideally both. /liliHands‑on exposure to knowledge‑based agentic systems, such as agents grounded in knowledge graphs, structured documents, domain rules, ontologies, databases, or retrieval systems. /liliExperience with methods for controlling reasoning or inference, such as guardrails, constrained planning, validation layers, policy‑based tool use, symbolic checks, or deterministic workflow components. /liliFamiliarity with neuro‑symbolic AI concepts or hybrid reasoning architectures. /liliExperience designing transparent, inspectable, or explainable AI methods. /liliPractical experience with agentic reasoning evaluation, testing, benchmarking, observability, or failure analysis. /liliFull‑stack web development experience, including backend APIs and frontend application development. /li /ul /p #J-18808-Ljbffr
