Lead AI Architect

Responsibilities

• Design and implement real‑world agentic AI systems using modern agent frameworks and orchestration tools.
• Develop 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.
• Work with knowledge‑based AI architectures, including retrieval‑augmented generation, knowledge graphs, symbolic rules, structured domain models, ontologies, and hybrid reasoning systems.
• Develop and apply mechanisms for controlling inference, including planning constraints, reasoning policies, guardrails, validation layers, tool‑use control, and human‑in‑the‑loop checkpoints.
• Explore and implement neuro‑symbolic approaches for agentic reasoning, combining LLM‑based reasoning with symbolic, rule‑based, graph‑based, or formally structured methods.
• Build transparent AI methods that make agent behaviour traceable, explainable, testable, and auditable.
• Create evaluation and testing frameworks for agentic systems, including benchmark tasks, regression tests, failure‑mode analysis, trace inspection, robustness testing, and task‑level performance measurement.
• Develop full‑stack prototypes and production applications, integrating backend services, APIs, databases, frontend interfaces, model providers, and orchestration layers.
• Collaborate with researchers, engineers, product teams, and domain experts to translate ambiguous real‑world problems into reliable agentic workflows.
• Stay current with developments in agentic AI, reasoning systems, LLM orchestration, AI evaluation, and applied neuro‑symbolic methods.

• Must have neuro symbolic reasoning experience.
• Strong multi‑project experience developing real‑world AI agents or agentic workflows.
• Demonstrated focus on agentic reasoning, including planning, decomposition, tool use, multi‑step inference, workflow execution, or autonomous task completion.
• Experience in either industrial AI development, academic research, or ideally both.
• Hands‑on exposure to knowledge‑based agentic systems, such as agents grounded in knowledge graphs, structured documents, domain rules, ontologies, databases, or retrieval systems.
• Experience with methods for controlling reasoning or inference, such as guardrails, constrained planning, validation layers, policy‑based tool use, symbolic checks, or deterministic workflow components.
• Familiarity with neuro‑symbolic AI concepts or hybrid reasoning architectures.
• Experience designing transparent, inspectable, or explainable AI methods.
• Practical experience with agentic reasoning evaluation, testing, benchmarking, observability, or failure analysis.
• Full‑stack web development experience, including backend APIs and frontend application development.