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Kandou

Lead AI Architect

Kandou

Lead AI Architect designing and deploying advanced AI agent systems for real-world applications. Collaborating across industrial and academic environments to innovate in agentic AI workflows.

Posted 6/29/2026full-timeSaint-Sulpice • 🇨🇭 SwitzerlandSeniorWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in developing and implementing agentic AI systems, focusing on neuro-symbolic reasoning, knowledge-based architectures, and transparent AI methods. Proficient in full-stack development and evaluation frameworks for agentic workflows.

Highest-signal resume keywords
Neuro-Symbolic Reasoning ExperienceAgentic Workflow DevelopmentKnowledge-Based Agentic SystemsTransparent AI Methods DesignFull-Stack Web Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Agent FrameworksAnalytical PipelinesKnowledge GraphsInference Control MechanismsPlanning ConstraintsHybrid Reasoning SystemsBenchmark TestingAPIs DevelopmentFrontend Application DevelopmentDatabase Integration
Soft Skills
CollaborationProblem-Solving
Tools & Technologies
Orchestration ToolsRetrieval-Augmented GenerationSymbolic RulesStructured Domain ModelsGuardrailsValidation LayersHuman-in-the-Loop Checkpoints
Industry Keywords
Agentic AIAI EvaluationNeuro-Symbolic AIAutonomous Task CompletionMulti-Step Inference

About the role

Key responsibilities & impact
  • 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

What you’ll need
  • 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.

Benefits

Comp & perks
  • Health insurance
  • Retirement plans
  • Paid time off
  • Flexible work arrangements
  • Professional development