Lead AI Architect

Role Overview

• 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.

Requirements

• 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.
• Strong Python engineering skills.
• Experience with modern LLM and agentic AI frameworks.
• Experience with backend development, APIs, databases, and cloud or deployment environments.
• Experience with frontend technologies such as React, Next.js, TypeScript, or similar frameworks.
• Familiarity with vector databases, graph databases, semantic search, structured data pipelines, or knowledge graph tooling.

Tech Stack

• Cloud
• JavaScript
• Next.js
• Python
• React
• TypeScript

Benefits

• Health insurance
• Professional development opportunities